This guide is designed to help you prepare for technical interviews conducted in JavaScript by providing real-life examples of the types of coding challenges and technical questions you can expect to be asked. Practicing with these and other role-specific technical questions, you’ll be well equipped to go into your next interview confident and prepared.

Another excellent resource to consider is CodeSignal Learn, a platform dedicated to learning and practicing technical skills, including JavaScript. Learn offers learning paths for mastering the basics of JavaScript, preparing for technical interviews in JavaScript, and hundreds of other technical topics.  

Get ready to debug your way to interviewing success—no ‘stack overflow’ can stop you now!

Jump to a section:

• Basic JavaScript interview questions for early career devs 
• Intermediate JavaScript interview questions
• Advanced JavaScript interview questions
• JavaScript interview questions for senior developers
• JavaScript interview questions by focus area
• Tricky JavaScript interview questions
• Most common JavaScript practice questions
• Next steps & resources

What you will need to start practicing and where to practice

Preparing for a JavaScript interview requires a combination of understanding key concepts and hands-on practice. To start, you should set up your coding environment, such as a local development setup with Node.js or a browser-based environment. Utilize online code editors like CodePen, JSFiddle, or Repl.it for quick experiments and practice. Make use of developer tools in browsers like Chrome DevTools to debug and optimize your code. 

You’ll also want to leverage a variety of JavaScript resources, such as documentation, tutorials, and coding blogs, to refresh your knowledge. Engage with practice platforms like CodeSignal Learn to solve coding challenges and algorithms. Consistent practice in these environments will enhance your problem-solving skills and prepare you for the technical aspects of your JavaScript interview.

Basic JavaScript interview questions for early career devs (0 to 2 years of experience) 

In your technical interview, junior-level JS developers should be prepared to demonstrate a basic understanding of JavaScript fundamentals. This includes proficiency in JavaScript syntax, such as variable declarations, function definitions, and control structures. You should also be comfortable with debugging basics, such as using the browser console for error tracking and code testing. You’ll want to have a good grasp of JavaScript data types, such as strings, numbers, and objects,and be able to solve simple algorithms efficiently. Below are a few examples of the types of questions you can expect to be asked.

Learning tip: Practice core JavaScript skills in a realistic cloud IDE before your next interview for assessment—Mastering Algorithms and Data Structures in JavaScript in CodeSignal Learn helps you do just that.

Variables and types

Question: Write a JavaScript function checkDataTypes that takes three parameters and returns an array containing the data type of each parameter.

Sample solution:

function checkDataTypes(param1, param2, param3) {

  return [typeof param1, typeof param2, typeof param3];

}

console.log(checkDataTypes(42, "hello", true)); // ["number", "string", "boolean"]

console.log(checkDataTypes(null, undefined, {})); // ["object", "undefined", "object"]

console.log(checkDataTypes([], function() {}, 3.14)); // ["object", "function", "number"]

Explanation of solution: The checkDataTypes function takes three parameters and uses the typeof operator to determine the data type of each parameter. It then returns an array containing the results. The typeof operator is a basic JavaScript feature that returns a string indicating the type of the operand. 

Question: Write a JavaScript function scopeTest that demonstrates the difference between var, let, and const within different scopes (global, function, and block scope). The function should return an object with the values of the variables declared in different scopes.

Sample solution:

function scopeTest() {

  var globalVar = "global var";

  let globalLet = "global let";

  const globalConst = "global const";

  function localScope() {

    var localVar = "local var";

    let localLet = "local let";

    const localConst = "local const";

    if (true) {

      var blockVar = "block var";

      let blockLet = "block let";

      const blockConst = "block const";

    }

    return {

      localVar: localVar,

      localLet: localLet,

      localConst: localConst,

      blockVar: blockVar,

      blockLet: typeof blockLet !== "undefined" ? blockLet : "blockLet is not defined",

      blockConst: typeof blockConst !== "undefined" ? blockConst : "blockConst is not defined"

    };

  }

  return {

    globalVar: globalVar,

    globalLet: globalLet,

    globalConst: globalConst,

    localScope: localScope()

  };

}

console.log(scopeTest());

Explanation of solution: ​​In this solution, the function scopeTest demonstrates variable declarations using var, let, and const at the global scope. Inside the nested function localScope, it declares variables using the same keywords within both function and block scopes. The var declaration is function-scoped and accessible throughout the localScope function, including within the block. The let and const declarations are block-scoped, so they are only accessible within the block where they are defined. The solution returns an object containing the values of the variables to illustrate the differences in scope.

Operators

Question: Write a JavaScript function compareSums that takes four numbers as input, adds the first two numbers together, and then compares the sum to the sum of the second two numbers. The function should return true if the first sum is greater than the second sum, and false otherwise.

Sample solution:

function compareSums(a, b, c, d) {

  let sum1 = a + b;

  let sum2 = c + d;

  return sum1 > sum2;

}

console.log(compareSums(5, 3, 2, 4)); // true

console.log(compareSums(1, 2, 3, 4)); // false

console.log(compareSums(10, 15, 20, 5)); // false

Explanation of solution: The compareSums function takes four parameters and calculates the sum of the first two (a and b) and the sum of the second two (c and d). It then uses the > comparison operator to check if the first sum is greater than the second sum and returns the result. 

Question: Write a JavaScript function logicalCheck that takes three boolean values as input. The function should return true if at least two of the three values are true, and false otherwise.

Sample solution:

function logicalCheck(a, b, c) {

  let count = 0;

  if (a) count += 1;

  if (b) count += 1;

  if (c) count += 1;

  return count >= 2;

}

console.log(logicalCheck(true, true, false)); // true

console.log(logicalCheck(false, true, false)); // false

console.log(logicalCheck(true, true, true)); // true

Explanation of solution: The logicalCheck function takes three boolean parameters and uses logical operators to determine how many of the values are true. It initializes a count variable and increments it for each true value. The function then checks if the count is greater than or equal to 2 and returns the result. 

Control structures

Question: Write a JavaScript function findFirstEven that takes an array of numbers as input and returns the first even number found in the array. If there are no even numbers, return null.

Sample solution:

function findFirstEven(numbers) {

  for (let i = 0; i < numbers.length; i++) {

    if (numbers[i] % 2 === 0) {

      return numbers[i];

    }

  }

  return null;

}

console.log(findFirstEven([1, 3, 7, 8, 5])); // 8

console.log(findFirstEven([1, 3, 7, 5])); // null

console.log(findFirstEven([2, 4, 6])); // 2

Explanation of solution: The findFirstEven function iterates through the input array using a for loop. Within the loop, it uses an if statement to check if the current number is even (i.e., divisible by 2 with no remainder). If an even number is found, it is returned immediately. If the loop completes without finding an even number, the function returns null.

Question: Write a JavaScript function getDayName that takes a number between 1 and 7 as input and returns the corresponding day of the week (1 for Monday, 7 for Sunday). If the input is not a valid number, the function should throw an error.

Sample solution:

function getDayName(dayNumber) {

  try {

    switch (dayNumber) {

      case 1:

        return "Monday";

      case 2:

        return "Tuesday";

      case 3:

        return "Wednesday";

      case 4:

        return "Thursday";

      case 5:

        return "Friday";

      case 6:

        return "Saturday";

      case 7:

        return "Sunday";

      default:

        throw new Error("Invalid day number");

    }

  } catch (error) {

    return error.message;

  }

}

console.log(getDayName(1)); // "Monday"

console.log(getDayName(7)); // "Sunday"

console.log(getDayName(0)); // "Invalid day number"

Explanation of solution: The getDayName function uses a switch statement to match the input number (dayNumber) to the corresponding day of the week. If the input is not a number between 1 and 7, the default case is executed, which throws an error with the message “Invalid day number”. The try-catch block is used to handle this error, catching it and returning the error message. 

Intermediate JavaScript interview questions (2 to 5 years of experience) 

As a mid-level JavaScript developer with 2-5 years of experience, you should expect technical interview questions that dive deeper into your understanding of more advanced JS concepts. Be prepared to tackle questions on asynchronous programming—like handling promises, async/await syntax, and managing callbacks. You’ll want to be able to show you have a strong grasp of ES6 features, like arrow functions, destructuring, and modules, too. You should be able to discuss and implement effective error handling strategies, both synchronously and asynchronously. Familiarity with Web APIs, including the Fetch API and DOM manipulation, will likely be tested. Lastly, you’ll likely be expected to have a solid understanding of framework basics, whether it’s React, Angular, or Vue.js, which are integral to modern JavaScript development.

Learning tip: Want to hone your React skills before your next interview? Front-End Engineering with React is a learning path in CodeSignal Learn that will take you through the core React skills that front-end JS devs need.

Functions and execution contexts

Question: Write a JavaScript function createCounter that returns an object with two methods: increment and getValue. The increment method should increase a private counter variable by 1, and the getValue method should return the current value of the counter. Demonstrate the usage of this function with both function declarations and function expressions.

Sample solution:

// Using function declaration

function createCounter() {

  let counter = 0;

  return {

    increment: function() {

      counter += 1;

    },

    getValue: function() {

      return counter;

    }

  };

}

const counter1 = createCounter();

counter1.increment();

counter1.increment();

console.log(counter1.getValue()); // 2

// Using function expression

const createCounterExpr = function() {

  let counter = 0;

  return {

    increment: function() {

      counter += 1;

    },

    getValue: function() {

      return counter;

    }

  };

};

const counter2 = createCounterExpr();

counter2.increment();

console.log(counter2.getValue()); // 1

Explanation of solution: The createCounter function demonstrates closures by encapsulating a private counter variable within the returned object. The increment method increases the counter, and the getValue method returns the current counter value. The function is implemented twice: once using a function declaration and once using a function expression.

Question: Write a JavaScript function createPerson that takes a name as an argument and returns an object with a method greet. The greet method should return a greeting message including the person’s name. Use an arrow function for the greet method to illustrate the this binding behavior of arrow functions.

Sample solution:

function createPerson(name) {

  return {

    name: name,

    greet: () => `Hello, my name is ${name}`

  };

}

const person1 = createPerson("Alice");

console.log(person1.greet()); // "Hello, my name is Alice"

const person2 = createPerson("Bob");

console.log(person2.greet()); // "Hello, my name is Bob"

Explanation of solution: The createPerson function returns an object with a greet method. This method is defined using an arrow function, which captures the this value from the surrounding context (the createPerson function). This ensures that the name property is correctly referenced within the greet method.

DOM manipulation and events

Question: Write a JavaScript function highlightElements that selects all <p> elements within a given container and adds a click event listener to each. When a paragraph is clicked, its background color should change to yellow. Demonstrate how this function works when passed an element ID as the container.

Sample solution:

function highlightElements(containerId) {

  const container = document.getElementById(containerId);

  const paragraphs = container.getElementsByTagName('p');

  for (let i = 0; i < paragraphs.length; i++) {

    paragraphs[i].addEventListener('click', function() {

      this.style.backgroundColor = 'yellow';

    });

  }

}

// HTML structure for demonstration

/*

<div id="content">

  <p>Paragraph 1</p>

  <p>Paragraph 2</p>

  <p>Paragraph 3</p>

</div>

*/

highlightElements('content');

Explanation of solution: The highlightElements function first selects the container element by its ID using getElementById. It then selects all <p> elements within the container using getElementsByTagName. A for loop is used to iterate over the paragraphs, adding a click event listener to each. The event listener changes the background color of the clicked paragraph to yellow. 

Question: Write a JavaScript function addListItem that dynamically creates a new list item (<li>) with specified text and appends it to an unordered list (<ul>) with a given ID. Implement event delegation so that clicking any list item displays an alert with its text content.

Sample solution:

function addListItem(ulId, text) {

  const ul = document.getElementById(ulId);

  const li = document.createElement('li');

  li.textContent = text;

  ul.appendChild(li);

}

function setupEventDelegation(ulId) {

  const ul = document.getElementById(ulId);

  ul.addEventListener('click', function(event) {

    if (event.target && event.target.nodeName === 'LI') {

      alert(event.target.textContent);

    }

  });

}

// HTML structure for demonstration

/*

<ul id="myList">    

    <li>content</li>

</ul>

*/

addListItem('myList', 'Item 1');

addListItem('myList', 'Item 2');

setupEventDelegation('myList');

Explanation of solution: The addListItem function creates a new <li> element with the specified text and appends it to the <ul> element with the given ID. The setupEventDelegation function sets up event delegation by adding a click event listener to the <ul> element. The event listener checks if the clicked target is an <li> element and, if so, displays an alert with the text content of the clicked list item. 

Advanced JavaScript interview questions (5 years experience or more)

Question: Write a JavaScript function debounce that takes a function func and a delay wait as arguments, and returns a debounced version of func. The debounced function should delay the execution of func until after wait milliseconds have elapsed since the last time the debounced function was invoked. Demonstrate how this function can be used to optimize performance by limiting the number of times a search input triggers an API call.

Sample solution:

function debounce(func, wait) {

  let timeout;

  return function(...args) {

    clearTimeout(timeout);

    timeout = setTimeout(() => func.apply(this, args), wait);

  };

}

// Example usage

function searchApi(query) {

  console.log(`API call with query: ${query}`);

}

const debouncedSearch = debounce(searchApi, 300);

// HTML structure for demonstration

/*

<input type="text" id="searchInput" placeholder="Search...">

*/

document.getElementById('searchInput').addEventListener('input', function(event) {

  debouncedSearch(event.target.value);

});

Explanation of solution: The debounce function creates a closure that maintains a timeout variable. When the returned function is invoked, it clears any existing timeout and sets a new one to call func after wait milliseconds. This ensures that func is called only once after a specified delay, even if the debounced function is called multiple times within that period. In the example usage, the debounced searchApi function is attached to an input field’s input event, optimizing performance by limiting the number of API calls made during rapid typing.

Question: Write a JavaScript function sanitizeInput that takes a string input and returns a sanitized version of the string to prevent Cross-Site Scripting (XSS) attacks. Then, demonstrate how to implement a scalable architecture to handle form submissions securely on both client-side and server-side.

Sample solution:

function sanitizeInput(input) {

  const element = document.createElement('div');

  element.textContent = input;

  return element.innerHTML;

}

// Example usage on client-side

document.getElementById('submitButton').addEventListener('click', function() {

  const userInput = document.getElementById('userInput').value;

  const sanitizedInput = sanitizeInput(userInput);

  console.log(`Sanitized Input: ${sanitizedInput}`);

  // Assume sendToServer is a function that sends data to the server

  sendToServer(sanitizedInput);

});

// Server-side (Node.js/Express example)

const express = require('express');

const app = express();

const bodyParser = require('body-parser');

const xssFilters = require('xss-filters');

app.use(bodyParser.urlencoded({ extended: true }));

app.use(bodyParser.json());

app.post('/submit', (req, res) => {

  const userInput = req.body.userInput;

  const sanitizedInput = xssFilters.inHTMLData(userInput);

  console.log(`Sanitized Input on Server: ${sanitizedInput}`);

  res.send(`Received sanitized input: ${sanitizedInput}`);

});

app.listen(3000, () => {

  console.log('Server running on port 3000');

});

Explanation of solution: The sanitizeInput function creates a div element, sets its textContent to the input string, and then retrieves the innerHTML, effectively escaping any potentially malicious code. On the client-side, this function is used to sanitize user input before sending it to the server. On the server-side, an Express application is set up to receive form submissions. The xss-filters library is used to sanitize input data, providing an additional layer of security.

JavaScript interview questions for senior developers (10+ years of experience)

Question: How would you architect a large-scale, cross-platform application using JavaScript to ensure maintainability, scalability, and high performance? Discuss the key considerations and technologies you would use.

Sample answer:

To architect a large-scale, cross-platform application using JavaScript, I would consider the following key aspects:

• Frontend framework: Utilize a modern frontend framework like React or Angular for building the user interface. These frameworks support component-based architecture, making it easier to maintain and scale the application.
• Backend framework: Use Node.js for the backend to leverage JavaScript’s full-stack capabilities. Frameworks like Express or NestJS can provide a robust foundation for developing scalable server-side applications.
• Cross-platform development: For mobile and desktop applications, consider using frameworks like React Native or Electron. React Native allows you to write code once and deploy it on both iOS and Android, while Electron can be used for cross-platform desktop applications.
• State management: Implement a state management library such as Redux or MobX to manage the application’s state efficiently, ensuring predictable state changes and improving maintainability.
• Microservices architecture: Adopt a microservices architecture for the backend to ensure scalability and flexibility. Each microservice can be developed, deployed, and scaled independently, reducing the risk of bottlenecks.
• API design: Use RESTful APIs or GraphQL to facilitate communication between the frontend and backend. GraphQL can be particularly beneficial for complex queries and reducing the number of API calls.
• Performance optimization: Employ techniques like lazy loading, code splitting, and server-side rendering (SSR) to optimize performance. Tools like Webpack can help with bundling and optimizing assets.
• Testing: Implement comprehensive testing strategies, including unit tests, integration tests, and end-to-end tests, using tools like Jest, Mocha, and Cypress.
• Continuous Integration and Deployment (CI/CD): Set up CI/CD pipelines to automate testing and deployment, ensuring quick and reliable releases. Tools like Jenkins, Travis CI, and GitHub Actions can be useful.
• Security: Implement security best practices, such as input validation, authentication, authorization, and secure data storage. Use libraries like Helmet.js for securing HTTP headers and OAuth for authentication.

Question: What strategies would you employ to optimize the performance of a legacy JavaScript application while managing technical debt and ensuring future scalability? Discuss your approach and the tools you would use.

Sample answer:

To optimize a legacy JavaScript application, I would start with a thorough code audit to identify bottlenecks and areas with high technical debt, refactoring for better readability and maintainability. Using performance profiling tools like Chrome DevTools and Lighthouse, I would analyze metrics such as load time and rendering performance. Optimizing asset delivery through minification, compression, and image optimization, leveraging tools like Webpack, would be my next step. Implementing lazy loading and code splitting would help reduce initial load times, and employing caching strategies, such as browser and server-side caching along with CDNs, would enhance performance.

Database optimization is crucial, so I would ensure queries and indexing are efficient, considering ORM tools for streamlined interactions. I would use asynchronous operations, utilizing Promises and async/await, to prevent blocking of the main thread and improve performance. Establishing robust monitoring and logging with tools like New Relic and Sentry would help track performance metrics and identify real-time issues.

To manage technical debt, I would prioritize critical issues and create a gradual refactoring plan. Lastly, to ensure scalability, I would employ microservices, containerization (Docker), and orchestration tools like Kubernetes, enabling efficient handling of increased load and traffic. This approach balances immediate performance gains with long-term maintainability and scalability.

JavaScript interview questions by focus area

JavaScript front-end interview questions

Question: Write a React component Counter that includes a button and a display of the current count. The count should start at 0 and increment by 1 each time the button is clicked. Use React’s useState hook for state management.

Sample solution:

import React, { useState } from 'react';

function Counter() {

  const [count, setCount] = useState(0);

  return (

    <div>

      <p>Current Count: {count}</p>

      <button onClick={() => setCount(count + 1)}>Increment</button>

    </div>

  );

}

export default Counter;

Explanation of solution: The Counter component uses React’s useState hook to manage the count state. The useState hook initializes count to 0 and provides a setCount function to update it. When the button is clicked, the onClick handler increments the count state by 1 using setCount.

Question: Create a simple React application with two routes: Home and About. Use React Router for client-side routing and ensure that both pages are accessible, including appropriate aria attributes.

Sample solution:

import React from 'react';

import { BrowserRouter as Router, Route, Link, Switch } from 'react-router-dom';

function Home() {

  return (

    <div>

      <h1>Home Page</h1>

      <p>Welcome to the home page!</p>

    </div>

  );

}

function About() {

  return (

    <div>

      <h1>About Page</h1>

      <p>Learn more about us on this page.</p>

    </div>

  );

}

function App() {

  return (

    <Router>

      <nav>

        <ul>

          <li>

            <Link to="/" aria-label="Home">Home</Link>

          </li>

          <li>

            <Link to="/about" aria-label="About">About</Link>

          </li>

        </ul>

      </nav>

      <Switch>

        <Route exact path="/" component={Home} />

        <Route path="/about" component={About} />

      </Switch>

    </Router>

  );

}

export default App;

Explanation of solution: The App component sets up client-side routing using React Router. The Router component wraps the entire application, and Switch handles the routing logic. Route components define the paths for Home and About pages, each rendering the respective component. The nav element contains Link components for navigation, with aria-label attributes for accessibility. 

JavaScript interview questions for automation testing

Question: Write a simple unit test for a JavaScript function add(a, b) that returns the sum of two numbers. Use the Jest testing framework.

Sample solution:

// add.js

function add(a, b) {

  return a + b;

}

module.exports = add;

// add.test.js

const add = require('./add');

test('adds 1 + 2 to equal 3', () => {

  expect(add(1, 2)).toBe(3);

});

test('adds -1 + -1 to equal -2', () => {

  expect(add(-1, -1)).toBe(-2);

});

Explanation of solution: The add function is a simple utility that returns the sum of two numbers. The unit tests are written using the Jest testing framework. The test function defines individual test cases, where the expect function is used to assert that the result of add(a, b) matches the expected value. 

Question: Write a simple end-to-end test using Selenium WebDriver for a web page with a login form. The form includes two inputs (username and password) and a submit button. The test should check that after entering the credentials and submitting the form, the user is redirected to a dashboard page.

Sample solution:

// login.test.js

const { Builder, By, until } = require('selenium-webdriver');

const assert = require('assert');

(async function loginTest() {

  let driver = await new Builder().forBrowser('chrome').build();

  try {

    await driver.get('http://localhost:3000/login');

    await driver.findElement(By.name('username')).sendKeys('testuser');

    await driver.findElement(By.name('password')).sendKeys('password123');

    await driver.findElement(By.css('button[type="submit"]')).click();

    await driver.wait(until.urlIs('http://localhost:3000/dashboard'), 5000);

    let currentUrl = await driver.getCurrentUrl();

    assert.strictEqual(currentUrl, 'http://localhost:3000/dashboard');

  } finally {

    await driver.quit();

  }

})();

Explanation of solution: This solution uses Selenium WebDriver for browser automation. The test script navigates to the login page, enters the username and password, and submits the form. It then waits until the URL changes to the dashboard page and asserts that the current URL is as expected.

JavaScript algorithm interview questions

Question: Write a JavaScript function mergeSort that sorts an array of numbers using the merge sort algorithm. Analyze the time and space complexity of your implementation.

Sample solution:

function mergeSort(arr) {

  if (arr.length <= 1) {

    return arr;

  }

  const mid = Math.floor(arr.length / 2);

  const left = mergeSort(arr.slice(0, mid));

  const right = mergeSort(arr.slice(mid));

  return merge(left, right);

}

function merge(left, right) {

  let result = [];

  let leftIndex = 0;

  let rightIndex = 0;

  while (leftIndex < left.length && rightIndex < right.length) {

    if (left[leftIndex] < right[rightIndex]) {

      result.push(left[leftIndex]);

      leftIndex++;

    } else {

      result.push(right[rightIndex]);

      rightIndex++;

    }

  }

  return result.concat(left.slice(leftIndex)).concat(right.slice(rightIndex));

}

// Example usage

const array = [38, 27, 43, 3, 9, 82, 10];

console.log(mergeSort(array)); // [3, 9, 10, 27, 38, 43, 82]

Explanation of solution: The mergeSort function recursively divides the array into halves until it has arrays of length 1 or 0. The merge function then combines these smaller arrays back together in sorted order. The time complexity of merge sort is O(n log n) because the array is split in half log n times, and merging the arrays takes linear time. The space complexity is O(n) due to the additional arrays created during the merge process. 

Question: Write a JavaScript function binaryTreePaths that takes the root of a binary tree and returns all root-to-leaf paths in the tree as an array of strings. Use recursion to solve this problem.

Sample solution:

function binaryTreePaths(root) {

  const paths = [];

  function dfs(node, path) {

    if (!node) return;

    path += node.val;

    if (!node.left && !node.right) {

      paths.push(path);

    } else {

      path += '->';

      dfs(node.left, path);

      dfs(node.right, path);

    }

  }

  dfs(root, '');

  return paths;

}

// Example usage

const tree = {

  val: 1,

  left: {

    val: 2,

    left: null,

    right: {

      val: 5,

      left: null,

      right: null

    }

  },

  right: {

    val: 3,

    left: null,

    right: null

  }

};

console.log(binaryTreePaths(tree)); // ["1->2->5", "1->3"]

Explanation of solution: The binaryTreePaths function uses a depth-first search (DFS) approach to traverse the binary tree. The dfs helper function is called recursively, building the path as it traverses the tree. When a leaf node is reached, the current path is added to the paths array. 

Tricky JavaScript interview questions

Question: Write a JavaScript function createExpensiveResource that simulates the creation of an expensive resource (e.g., a large array). Use closures to manage access to this resource and implement a method to release it properly to prevent memory leaks.

Sample solution:

function createExpensiveResource() {

  let resource = new Array(1000000).fill('some data');

  function accessResource() {

    if (!resource) {

      console.log("Resource has been released.");

      return;

    }

    return resource;

  }

  function releaseResource() {

    resource = null;

    console.log("Resource has been released.");

  }

  return {

    access: accessResource,

    release: releaseResource

  };

}

// Example usage

const resourceManager = createExpensiveResource();

console.log(resourceManager.access()); // Access the resource

resourceManager.release(); // Release the resource

console.log(resourceManager.access()); // Try to access the released resource

Explanation of solution: The createExpensiveResource function creates a large array and uses closures to provide controlled access to it. The accessResource function allows access to the resource, while the releaseResource function sets the resource to null, freeing up memory. This solution demonstrates closure applications and how to prevent memory leaks by properly releasing resources.

Why it’s tricky: This question is tricky because it tests your understanding of closures and how they can inadvertently cause memory leaks if resources are not properly managed. It requires knowledge of both resource management and the use of closures to control access to variables.

Question: Explain the output of the following JavaScript code and why it behaves that way. Discuss the concepts of the event loop and the concurrency model that affect the output.

console.log('Start');

setTimeout(() => {

  console.log('Timeout');

}, 0);

Promise.resolve().then(() => {

  console.log('Promise');

});

console.log('End');

Sample output:

Start

End

Promise

Timeout

Sample answer:

The output of the code is determined by JavaScript’s event loop and concurrency model. When the script runs:

1. console.log('Start') is executed first, printing “Start”.
2. setTimeout is called with a delay of 0 milliseconds, which schedules the callback to be executed in the next iteration of the event loop.
3. Promise.resolve().then is called, which schedules the callback to be executed after the current execution context finishes, before the next event loop iteration.
4. console.log('End') is executed next, printing “End”.
5. After the current execution context finishes, the microtask queue (containing the resolved promise callback) is processed first.
6. The macrotask queue (containing the setTimeout callback) is then processed. Thus, “Promise” is printed before “Timeout”.

Why it’s tricky: This question is tricky because it explores the intricacies of JavaScript’s event loop and concurrency model. Understanding the order of execution between synchronous code, microtasks (promises), and macrotasks (setTimeout) requires you to have a deep understanding of how JavaScript handles asynchronous operations and task scheduling.

Most common JavaScript practice questions (if you have limited time)

In JavaScript interviews, you will often face a variety of question types designed to assess your technical skills and problem-solving abilities. Common algorithm problems, which require you to demonstrate your understanding of data structures and algorithmic efficiency, are a staple of JavaScript interviews. You’ll likely also be asked about JavaScript quirks, such as type coercion and scope behavior, to gauge your depth of knowledge about the language. Coding challenges are another popular format, often presented in real-time coding environments, where you must solve complex problems using key JavaScript methods. Interview cheat sheets can be valuable resources for quick reference on syntax and common functions. 

Question: Write a JavaScript function findDuplicates that takes an array of numbers and returns an array of duplicate numbers. Ensure that each duplicate number appears only once in the output array.

Sample solution:

function findDuplicates(arr) {

  const seen = new Set();

  const duplicates = new Set();

  for (let num of arr) {

    if (seen.has(num)) {

      duplicates.add(num);

    } else {

      seen.add(num);

    }

  }

  return Array.from(duplicates);

}

// Example usage

console.log(findDuplicates([1, 2, 3, 1, 2, 4])); // [1, 2]

console.log(findDuplicates([5, 5, 5, 5, 5])); // [5]

console.log(findDuplicates([1, 2, 3, 4, 5])); // []

Explanation of solution: The findDuplicates function uses two sets: seen to track numbers that have already been encountered, and duplicates to track numbers that appear more than once. The function iterates through the array, adding numbers to seen and, if a number is already in seen, adding it to duplicates. The function finally returns an array created from the duplicates set.

Question: Explain the difference between null and undefined in JavaScript. Provide examples to illustrate the key differences.

Sample solution:

null and undefined are both JavaScript primitives representing the absence of a value, but they have different meanings and uses. undefined indicates that a variable has been declared but has not yet been assigned a value. null is an assignment value that represents no value or an empty value. For example:

let uninitializedVar; // undefined

let emptyVar = null; // null

console.log(typeof uninitializedVar); // "undefined"

console.log(typeof emptyVar); // "object"

console.log(uninitializedVar == null); // true

console.log(uninitializedVar === null); // false

console.log(emptyVar == undefined); // true

console.log(emptyVar === undefined); // false

Explanation of solution: In this example, uninitializedVar is declared but not assigned a value, so it is undefined. emptyVar is explicitly assigned the value null. The typeof operator shows that undefined is its own type, while null is considered an object due to a historical bug in JavaScript. The comparison examples demonstrate that == treats both null and undefined as equal, while === does not. 

Question: Write a JavaScript function capitalizeWords that takes a string and returns a new string with the first letter of each word capitalized.

Sample solution:

function capitalizeWords(str) {

  return str.split(' ').map(word => word.charAt(0).toUpperCase() + word.slice(1)).join(' ');

}

// Example usage

console.log(capitalizeWords('hello world')); // "Hello World"

console.log(capitalizeWords('javascript is fun')); // "Javascript Is Fun"

console.log(capitalizeWords('capitalize each word')); // "Capitalize Each Word"

Explanation of solution: The capitalizeWords function splits the input string into an array of words, capitalizes the first letter of each word, and then joins the words back into a single string. The split, map, charAt, toUpperCase, and slice methods are used to transform the string. 

Next steps & resources

JavaScript development is a dynamic, exciting field that merges creative problem-solving with the powerful coding capabilities of JS and JS libraries and frameworks. And, it pays well: according to Glassdoor, JavaScript developers in the US earn an average salary of over $115,000 per year. While securing a JavaScript developer role can be challenging—especially in today’s competitive job market—being well-prepared for the interview can significantly improve your chances.

Whether you’re aiming for a career as a JavaScript developer or looking to enhance your coding skills first, the next step is simple and free: check out the JavaScript learning paths in CodeSignal Learn. You’ll be tackling real-world JavaScript problems and refining your technical skills right away. Start your journey with CodeSignal Learn for free today and prepare for your next JavaScript interview—or explore dozens of other technical skill areas.

The post 25 JavaScript interview questions (and answers) from basic to senior level appeared first on CodeSignal.

With rising demand for data scientists in sectors such as healthcare and finance, it pays to land a data science role. According to Glassdoor, the average salary for a data scientist in the US is $154,655 per year. But, getting hired for this role can be competitive and challenging. This guide is designed to support your interview preparation goals through 30 job-relevant practice questions, covering everything from basic concepts to advanced scenarios typical of senior roles.

Get ready to ace your next interview with our comprehensive guide. Whether you’re applying for a junior-level position or aiming for a leadership role, these questions will prepare you to show off your data skills and impress your future employers.

Jump to a section:

• Basic level questions
• Advanced level questions
• Technical data science interview questions
  • Python data science interview questions
  • Pandas data science interview questions
  • R data science interview questions
  • SQL questions for data science interviews
  • Big data questions for data science roles
  • Machine learning data science questions
  • AI and automation data science questions
  • Data collection and data processing questions
  • Statistics and probability interview questions
  • A/B testing questions for data science interviews
• Non-technical data science interview questions
• Next steps & resources

Basic level questions

When preparing for an entry-level data science interview, you’ll encounter questions that cover the fundamental concepts of data science basics. These questions aim to assess your foundational knowledge and understanding of core principles essential to the field. 

Here are some topics that basic-level interview questions may cover:

• Statistical analysis: Understanding descriptive and inferential statistics.
• Data manipulation: Basic methods of cleaning, sorting, and organizing data.
• Programming skills: Familiarity with Python or R for simple tasks.
• Problem solving: Demonstrating logical thinking through hypothetical data scenarios.

Learning tip: Looking to build your technical skills in data science before interviewing for a role? CodeSignal Learn’s Journey into Data Science with Python learning path takes you through using popular data science libraries like NumPy and pandas, creating data visualizations, and using ML algorithms in 7 practice-based courses. 

Advanced level questions

In a senior-level data science interview, you’ll be faced with advanced questions designed to challenge your expertise and test your ability to solve real-world data challenges. These questions demand advanced analytical skills and a deep understanding of senior-level data science topics, emphasizing your problem-solving skills and decision-making capabilities. Mastery of these elements is crucial as they allow you to handle intricate analyses and develop innovative solutions that directly impact business outcomes.

Here are some topics that advanced-level interview questions may cover:

• Advanced machine learning: Deep knowledge of algorithms, including supervised and unsupervised learning, neural networks, and ensemble methods.
• Big data technologies: Proficiency in handling large datasets using technologies like Hadoop, Spark, and Kafka.
• Statistical modeling: Detailed discussions on predictive modeling, time series analysis, and experimental design.
• Data architecture: Understanding of how to structure data pipelines and optimize data storage for efficient querying and analysis.
• AI and automation: Insights into the integration of artificial intelligence techniques to automate data processes and enhance predictive analytics.

These topics reflect the sophisticated nature of senior-level roles, where you are expected to lead projects, design data strategies, and provide actionable insights that significantly impact business outcomes.

Technical data science interview questions

In your data science interview, you’ll be tested on a variety of technical skills commonly used in the role. Expect questions that assess your proficiency with querying languages like SQL and programming languages such as Python or R, which are often used for data manipulation and analysis. You’ll likely also discuss how you apply statistical methods and machine learning algorithms as they relate to real-world data challenges.

Python data science interview questions

In your data science interview, expect to demonstrate your Python coding skills through a variety of questions focused on Python for data analysis and scripting. You’ll need to demonstrate your familiarity with essential Python libraries like NumPy, pandas, and Matplotlib, which are critical for manipulating datasets and creating visualizations. 

Python data structures

Question: Can you explain how you would use Python lists and dictionaries to manage data in a data science project? Provide an example of how you might implement these structures.

Sample answer: Python lists and dictionaries are fundamental for managing data efficiently in Python scripts. For instance, I often use lists to store sequential data and dictionaries for key-value pairs, which is useful for categorizing or indexing data without using external libraries. An example would be reading raw data from a CSV file line by line, storing each line as a list, and then aggregating counts or other metrics in a dictionary where keys represent categories or unique identifiers from the data. 

Difficulty: Basic

Basic Python scripting for data processing

Question: Describe a scenario where you would write a Python script to process and analyze raw text data. What steps would you take in your script?

Sample answer: In a scenario where I need to process raw text data, such as customer feedback, I would write a Python script that reads text files, cleanses the text by removing special characters and stopwords, and then analyzes frequency of words or phrases. The script would start by opening and reading files using a loop, then apply transformations to clean the text using Python’s string methods. Finally, I would use Python’s built-in functions or a simple loop to count occurrences of each word or phrase, storing the results in a dictionary for later analysis or reporting. 

Difficulty: Basic

Data visualization with matplotlib

Question: Write a Python script using matplotlib to create a bar chart that compares the average monthly sales data for two years. The sales data for each month should be represented as two bars side by side, one for each year. Include labels for each month, add a legend to differentiate between the two years, and title the chart ‘Comparison of Monthly Sales’.

Sample answer: 

import matplotlib.pyplot as plt

# Sample data

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

sales_2020 = [200, 180, 240, 300, 280, 350, 370, 360, 390, 420, 450, 470]

sales_2021 = [210, 190, 250, 310, 290, 360, 380, 370, 400, 430, 460, 480]

# Creating the bar chart

x = range(len(months))  # the label locations

width = 0.35  # the width of the bars

fig, ax = plt.subplots()

rects1 = ax.bar(x, sales_2020, width, label='2020')

rects2 = ax.bar([p + width for p in x], sales_2021, width, label='2021')

# Add some text for labels, title, and custom x-axis tick labels, etc.

ax.set_xlabel('Month')

ax.set_ylabel('Sales')

ax.set_title('Comparison of Monthly Sales')

ax.set_xticks([p + width / 2 for p in x])

ax.set_xticklabels(months)

ax.legend()

# Function to add labels on bars

def autolabel(rects, ax):

    for rect in rects:

        height = rect.get_height()

        ax.annotate('{}'.format(height),

                    xy=(rect.get_x() + rect.get_width() / 2, height),

                    xytext=(0, 3),  # 3 points vertical offset

                    textcoords="offset points",

                    ha='center', va='bottom')

autolabel(rects1, ax)

autolabel(rects2, ax)

plt.show()

Difficulty: Advanced

Advanced data structures and algorithms

Question: Write a Python function that takes a list of integers and returns a new list with only the unique elements from the original list, but in the same order they first appeared. You should not use any additional libraries like pandas or numpy.

Sample answer: 

def unique_elements(nums):

    seen = set()

    unique = []

    for num in nums:

        if num not in seen:

            unique.append(num)

            seen.add(num)

    return unique

# Example usage

print(unique_elements([1, 2, 2, 3, 4, 3, 1, 5]))

Difficulty: Advanced

Learning tip: For more Python practice questions, check out our guide to preparing for a Python interview. 

Pandas data science interview questions

Pandas is a powerful Python library for data manipulation and analysis, providing data structures and functions that make it easy to clean, analyze, and visualize complex datasets efficiently in data science. If the role you’re interviewing for expects you to use the pandas library, you’ll want to be proficient with DataFrames and Series, which are the backbone data structures in pandas. You’ll also want to know how to use pandas for large datasets, as well as its comprehensive tools for data cleaning and manipulation.

Data cleaning with pandas

Question: Given a pandas DataFrame df with columns ‘Date’, ‘Sales’, and ‘Customer_Rating’, write a Python code snippet to clean this DataFrame. Assume there are missing values in ‘Customer_Rating’ and duplicate rows across all columns. Remove duplicates and replace missing values in ‘Customer_Rating’ with the average rating.

Sample answer: 

import pandas as pd

# Assuming df is already defined and loaded with data

# Remove duplicate rows

df = df.drop_duplicates()

# Replace missing values in 'Customer_Rating' with the column's mean

df.fillna({'Customer_Rating': df['Customer_Rating'].mean()}, inplace=True)

Difficulty: Basic

Advanced data manipulation with pandas

Question: You have a pandas DataFrame df containing three years of hourly sales data with columns ‘Date_Time’ (datetime) and ‘Sales’ (float). Write a Python code snippet to resample this data to a weekly format and compute the total sales and average sales per week.”

Sample answer:

import pandas as pd

# Assuming df is already defined and loaded with data

# Ensure 'Date_Time' column is in datetime format

df['Date_Time'] = pd.to_datetime(df['Date_Time'])

# Set 'Date_Time' as the DataFrame index

df.set_index('Date_Time', inplace=True)

# Resample data to weekly, calculate sum and mean of 'Sales'

weekly_sales = df.resample('W').agg({'Sales': ['sum', 'mean']})

# Renaming columns for clarity

weekly_sales.columns = ['Total_Weekly_Sales', 'Average_Weekly_Sales']

Difficulty: Advanced

Learning tip: Want more practice using pandas? Check out the Deep Dive into NumPy and Pandas learning path in CodeSignal Learn, created specifically for data scientists.

R data science interview questions

R is a programming language and software environment specifically designed for statistical computing and graphics, widely used in data science for data analysis, modeling, and visualization. If the role you’re applying for expects you to use R, you should be comfortable with R’s syntax, common functions, and packages such as ggplot2 and dplyr, which are useful for data manipulation and creating insightful graphical representations.

Data manipulation with dplyr

Question: Using R and the dplyr package, write a code snippet to filter a dataframe df containing columns ‘Age’, ‘Income’, and ‘State’. You need to select only those rows where ‘Age’ is greater than 30 and ‘Income’ is less than 50000. Then, arrange the resulting dataframe in descending order of ‘Income’.

Sample answer:

library(dplyr)

# Assuming df is already defined and loaded with data

result <- df %>%

  filter(Age > 30, Income < 50000) %>%

  arrange(desc(Income))

Difficulty: Basic

Creating plots with ggplot2

Question: Write a code snippet using R and ggplot2 to create a scatter plot of df with ‘Age’ on the x-axis and ‘Income’ on the y-axis. Color the points by ‘State’ and add a title to the plot.

Sample answer:

library(ggplot2)

# Assuming df is already defined and loaded with data

ggplot(df, aes(x=Age, y=Income, color=State)) +

  geom_point() +

  ggtitle("Scatter Plot of Age vs. Income Colored by State")

Difficulty: Basic

Complex data manipulation and visualization in R

Question: You are provided with a data frame in R named sales_data, containing columns Year, Month, Product, and Revenue. Write an R script to calculate the monthly average revenue for each product over all years and create a line plot of these averages over the months. Ensure that each product has a unique line with different colors and include a legend to identify the products.

Sample answer: 

library(dplyr)

library(ggplot2)

library(plotly)

# Assuming sales_data is already defined and loaded with data

# Calculating monthly average revenue for each product over all years

monthly_averages <- sales_data %>%

  group_by(Product, Month) %>%

  summarise(Average_Revenue = mean(Revenue, na.rm = TRUE)) %>%

  ungroup()

# Creating a line plot

p <- ggplot(monthly_averages, aes(x = Month, y = Average_Revenue, color = Product, group = Product)) +

  geom_line() +

  labs(title = "Monthly Average Revenue by Product", 

       x = "Month", 

       y = "Average Revenue") +

  scale_x_continuous(breaks = 1:12, labels = month.name[1:12]) + # assuming Month is numeric 1-12

  theme_minimal() +

  theme(legend.title = element_blank()) +

  guides(color = guide_legend(title = "Product"))

# Display the plot

ggplotly(p)

Difficulty: Advanced

Learning tip: Looking to build basic proficiency in R? CodeSignal Learn’s Data Analysis 101 with R learning path is an accessible and engaging introduction to the R programming language relevant to data scientists. 

SQL questions for data science interviews

SQL (Structured Query Language) is a programming language used for managing and manipulating relational databases, widely utilized in data science for querying, aggregating, and transforming large datasets to extract insights. When applying for a data science role, you should be prepared to demonstrate SQL skills such as writing complex queries, optimizing query performance, and understanding how to join multiple tables to efficiently extract and analyze data from relational databases.

SQL commands and query optimization

Question: Describe how you would use SQL commands to improve the performance of a data query in a large relational database. What specific techniques would you apply for query optimization?

Sample answer: To improve query performance in a large relational database, I utilize several SQL commands and optimization techniques. First, I make use of ‘EXPLAIN’ to understand the query plan and identify bottlenecks like full table scans or inefficient joins. For optimization, I often apply indexing on columns that are frequently used in WHERE clauses and JOIN conditions to speed up data retrieval. Additionally, I use subqueries and temporary tables strategically to simplify complex queries and reduce the computational load. 

Difficulty: Basic

Database management

Question: How do you ensure that your SQL queries are both efficient and effective in extracting insights from a relational database? Can you give an example of a complex SQL query you’ve written?

Sample answer: Efficiency in SQL for data science involves writing queries that will run fast and pull the right data to drive insights. I ensure this by understanding the database schema and relationships within the relational database, which helps in writing accurate SQL commands. For example, in a past project, I had to analyze customer behavior across multiple products. I used SQL to join several tables—customers, transactions, and products—while filtering specific time frames and product categories. This involved complex JOIN clauses and WHERE conditions to extract a dataset that accurately represented purchasing patterns, which we then used for further analysis like segmentation and trend identification. For managing databases, I regularly check query performances and refactor them for better efficiency, ensuring that the data extraction process remains robust and reliable for ongoing analysis.

Difficulty: Advanced

Learning tip: Want a refresher on using SQL before your next interview? Journey into SQL with Taylor Swift, on CodeSignal Learn, is a fun, quick, and engaging learning path that uses Taylor Swift’s discography as your database.  

Big data questions for data science roles

Data processing with Apache Spark

Question: Using PySpark, write a code snippet to read a large dataset from HDFS, filter out records where the ‘status’ column is ‘inactive’, and then calculate the average ‘sale_amount’ for each ‘product_category’. Output the result as a DataFrame.

Sample answer:

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, avg

# Initialize Spark Session

spark = SparkSession.builder.appName("SalesDataAnalysis").getOrCreate()

# Load data from HDFS

df = spark.read.format("parquet").load("hdfs://path_to_dataset")

# Filter inactive records and calculate average sale amount per product category

active_df = df[df['status'] != "inactive"].drop(columns=’status’)

active_df.groupby("product_category").agg('mean')

# Show the result

result_df.show()

# Stop the Spark session

spark.stop()

Difficulty: Advanced

Real-time data processing with Apache Kafka and Spark Streaming

Question: Write a PySpark Streaming application that consumes messages from a Kafka topic named ‘user_logs’, extracts the fields ‘user_id’ and ‘activity’, and counts the number of each activity type per user in real-time. Display the counts on the console as they are updated.

Sample answer: 

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, from_json

from pyspark.sql.types import StructType, StringType

# Initialize Spark Session

spark = SparkSession.builder \

    .appName("RealTimeUserActivity") \

    .getOrCreate()

# Define schema for Kafka data

schema = StructType().add("user_id", StringType()).add("activity", StringType())

# Create DataFrame representing the stream of input lines from Kafka

df = spark \

    .readStream \

    .format("kafka") \

    .option("kafka.bootstrap.servers", "localhost:9092") \

    .option("subscribe", "user_logs") \

    .load() \

    .selectExpr("CAST(value AS STRING) as json_str") \

    .select(from_json(col("json_str"), schema).alias("data")) \

    .select("data.*")

# Count each activity type per user in real-time

activityCounts = df.groupBy("user_id", "activity").count()

# Start running the query to print the running counts to the console

query = activityCounts \

    .writeStream \

Difficulty: Advanced

Machine learning data science questions

Model selection

Question: How do you decide which machine learning model to use for a specific problem? For instance, how would you approach a dataset predicting customer churn?

Sample answer: When deciding on a model, I start by considering the nature of the data, the problem type (classification or regression), and the interpretability required by stakeholders. Predicting customer churn is a binary classification problem, so I might start with logistic regression for its simplicity and interpretability. I would also consider tree-based models like Random Forest or Gradient Boosting Machines for their robustness and ability to handle non-linear relationships. I typically compare a few models based on their performance metrics like accuracy, ROC-AUC, and F1-score, and validate them using techniques like cross-validation before making a final decision.

Difficulty: Basic

Handling overfitting

Question: What strategies do you employ to prevent overfitting in a machine learning model?

Sample answer: To prevent overfitting, I use several techniques depending on the model and data. First, I might split the data into training, validation, and test sets to monitor and prevent overfitting during model training. Regularization methods such as L1 or L2 regularization are also effective, especially in regression models. For decision trees, I control overfitting by setting limits on tree depth, minimum samples per leaf, and other parameters. And ensemble methods like bagging and boosting can reduce overfitting by building more robust models from multiple learning algorithms.

Difficulty: Advanced

Model evaluation

Question: Describe how you evaluate the performance of a machine learning model. Can you give an example of how you’ve applied these evaluation techniques in a past project?

Sample answer: I evaluate machine learning models using several key performance metrics. For classification tasks, I look at accuracy, precision, recall, F1-score, and the ROC-AUC curve. For regression, I consider metrics like RMSE and MAE. In a past project aimed at predicting real estate prices, I used RMSE to measure the average error between the predicted prices and the actual prices. I also used cross-validation to ensure that the model’s performance was consistent across different subsets of the data. These metrics helped us fine-tune the model iteratively, which led to more reliable predictions.

Difficulty: Advanced

Application of probability in machine learning 

Question: How would you use probability theory to improve the performance of a machine learning model? Please explain with an example where you’ve implemented such techniques in past projects.

Sample answer: Probability theory is crucial for understanding and designing machine learning models, especially in classification problems where we estimate the probability of class memberships. For instance, in logistic regression, we use probability to estimate the likelihood that a given input point belongs to a certain class. This helps in assessing the confidence level of the predictions made by the model. In a past project, I improved model performance by integrating Bayesian probability to continually update the model as new data became available.

Difficulty: Advanced

Learning tip: Boost your ML skills before you apply to your next role with CodeSignal Learn’s Journey into Machine Learning with Sklearn and Tensorflow learning path. This series of 5 courses builds your skills in using ML to clean and preprocess data, create features, train neural networks, and more.

AI and automation data science questions

Predictive analytics in AI

Question: Can you describe how you would use AI to improve the predictive analytics process within a company? Specifically, how would AI enhance the accuracy and efficiency of forecasting models?

Sample answer: AI can significantly enhance predictive analytics by incorporating more complex algorithms, such as deep learning, that are capable of identifying non-linear relationships and interactions that traditional models might miss. For instance, I would use recurrent neural networks (RNNs) or LSTM (Long Short-Term Memory) networks for forecasting sales data, as they are particularly good with sequences and can predict based on the historical data trends. Additionally, AI can automate the feature engineering process, using techniques like feature selection and dimensionality reduction to improve model accuracy and efficiency.

Difficulty: Advanced

Learning tip: New to predictive analytics? The Predictive Modeling with Python path in CodeSignal Learn teaches you how to build and refine machine learning models, with a focus on regression models for prediction.

Building an AI-driven data processing system

Question: Write a Python script that uses an AI model to classify text data into categories. Assume you have a pre-trained model loaded as model and a list of text data called text_samples. Use the model to predict categories and print the results.

Sample answer:

# Assuming model is pre-loaded and ready to predict

# and text_samples is a pre-defined list of text data

import numpy as np

# Simulating text_samples list for demonstration

text_samples = ["This is a sample text about sports.", "Here is another one about cooking.", "This one discusses technology."]

# Function to preprocess text (actual preprocessing steps depend on model requirements)

def preprocess_text(texts):

    # Example preprocessing: converting list to numpy array for model compatibility

    # This could also include tokenization, lowercasing, removing punctuation, etc.

    return np.array(texts)

# Preprocessing the text data

preprocessed_texts = preprocess_text(text_samples)

# Predicting categories using the AI model

predictions = model.predict(preprocessed_texts)

# Printing results

for text, category in zip(text_samples, predictions):

    print(f'Text: "{text}" - Predicted Category: {category}')

Difficulty: Advanced

Data collection and data processing questions

Data collection and management

Question: You are tasked with designing a data collection strategy for a new app that tracks user interactions with various features. What factors would you consider when deciding what data to collect, and how would you ensure the data remains manageable and useful for analysis?

Sample answer: When designing a data collection strategy for the app, I would first identify the key metrics that align with our business objectives, such as user engagement times, frequency of feature use, and user feedback scores. I would ensure that the data collected is both relevant and sufficient to inform decision-making without collecting unnecessary information that could complicate processing and storage. To keep the data manageable, I would implement a schema that organizes data into structured formats and use automation tools to clean and preprocess the data as it comes in. This could involve setting up pipelines that automatically remove duplicates, handle missing values, and ensure data integrity. 

Difficulty: Basic

Data cleaning and preprocessing

Question: You receive a dataset containing customer transaction data over the past year. The dataset is incomplete with numerous missing values and some duplicate entries. How would you go about cleaning this data to prepare it for analysis?

Sample answer: To clean the dataset, I would first assess the extent and nature of the missing values. For categorical data, I might impute missing values using the mode or a predictive model, whereas for numerical data, I might use mean, median, or regression imputation, depending on the distribution and the amount of missing data. To address duplicates, I would identify unique transaction identifiers or a combination of variables (like date, time, and customer ID) that can confirm a transaction’s uniqueness. I would then remove duplicates based on these identifiers. After handling missing values and duplicates, I would validate the data for consistency and accuracy, ensuring that all data types are correct and that there are no illogical data entries, such as negative transaction amounts. To do this, I’d use both automated scripts for bulk cleaning and manual checks for nuanced errors. Finally, I’d document the cleaning process to allow for reproducibility and maintain a clean dataset for future analysis.

Difficulty: Basic

Statistics and probability interview questions

Understanding statistical distributions

Question: Could you describe a scenario where a Poisson distribution would be more appropriate to model an event than a normal distribution? How would you apply this in a data-driven decision-making process?

Sample answer: A Poisson distribution is ideal for modeling the number of times an event happens in a fixed interval of time or space when these events occur with a known constant mean rate and independently of the time since the last event. For example, it could model the number of users visiting a website per minute. This differs from a normal distribution, which is used for continuous data and where we’re looking at the distribution of means rather than actual event counts. In a business context, I’d use Poisson to predict customer arrivals or fault rates in a time frame.

Difficulty: Basic

Statistical inference

Question: Imagine you’re tasked with evaluating the effectiveness of two different marketing campaigns. What statistical test would you use to determine which campaign was more successful, and why?

Sample answer: To evaluate the effectiveness of two marketing campaigns, I would use a hypothesis test, specifically an independent samples t-test, if the data is normally distributed. This test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. I would set up the null hypothesis to assume no difference between the campaigns’ effects, and the alternative hypothesis to indicate a significant difference. The result would inform whether any observed difference in campaign performance is statistically significant or not.

Difficulty: Basic

Probability

Question: Imagine you are given a standard deck of 52 cards. What is the probability of drawing an ace followed by a king, without replacement? Please explain your steps.

Sample answer: To find the probability of drawing an ace followed by a king from a standard deck of 52 cards without replacement, we start by calculating the probability of drawing one of the four aces from the deck. This probability is 4/52, which simplifies to 1/13. Once an ace is drawn, there are now 51 cards left in the deck, including four kings. The probability of then drawing a king is 4/51. Therefore, the probability of both events happening in sequence is the product of the two individual probabilities: about 0.603% 

Difficulty: Basic

Advanced statistical methods

Question: Discuss a complex statistical method you have used in your data analysis. How did you decide that this method was the best choice, and what were the outcomes of applying this method?

Sample answer: In a recent project, I applied a mixed-effects model to account for both fixed and random effects in our data, which involved repeated measures from the same subjects. This method was chosen because it allowed us to understand both the fixed effects of the interventions we tested and the random effects due to individual differences. It was particularly useful for dealing with the non-independence of observations, which is a common issue in longitudinal data. The analysis provided insights into how different variables influenced our outcomes over time to guide more tailored interventions.

Difficulty: Advanced

A/B testing questions for data science interviews

Experimental design

Question: Can you walk me through how you would design an A/B test for a new product feature on a website? What steps would you take to ensure the results are statistically significant?

Sample answer: When designing an A/B test for a new product feature, I would start by defining clear metrics of success, such as conversion rate or user engagement time. I would then randomly assign users to two groups, ensuring each has a similar demographic makeup. The test would run long enough to collect sufficient data, using statistical power calculations to determine this duration. Lastly, I’d analyze the results using a hypothesis test—such as a chi-square test or a t-test, depending on the distribution and nature of the data—to determine if there’s a statistically significant difference between the two groups’ performance.

Difficulty: Basic

Interpreting results of an A/B test

Question: After running an A/B test on two different email marketing campaigns, Campaign A resulted in a 15% click-through rate (CTR) while Campaign B resulted in a 10% CTR. What conclusions can you draw from these results, and what would be your next steps?

Sample answer: From the results of the A/B test, it appears that Campaign A performed better than Campaign B. This suggests that the elements or messaging used in Campaign A were more effective in engaging users and encouraging them to click on the links provided. My next steps would be to analyze the specific components of Campaign A to understand what drove the higher engagement, such as the email subject line, graphics, or call-to-action. I would also recommend further testing to confirm these results over multiple iterations and different user segments to ensure that the observed difference wasn’t due to external factors or variances in the audience groups. If the results remain consistent, I would consider applying the successful elements of Campaign A to other marketing materials and strategies to potentially improve overall marketing effectiveness.

Difficulty: Basic

Non-technical data science interview questions

Communication with stakeholders

Question: Data science often involves collaboration with various stakeholders. Can you describe a situation where you had to explain a complex data science concept or finding to a non-technical audience? What approach did you take?

Sample answer: In one of my previous roles, I was responsible for presenting monthly performance metrics derived from our predictive models to the marketing team, who were not familiar with data science. To effectively communicate these complex concepts, I used metaphors and analogies related to common experiences, like predicting the weather, to explain how predictive models work. I also created visualizations and dashboards that illustrated the data in an intuitive way, showing trends and patterns without getting into the statistical details. 

Difficulty: Basic

Ethical considerations

Question: Data science can sometimes present ethical challenges. Can you talk about a time when you faced an ethical dilemma in your work? How did you handle it?

Sample answer: At a previous job, I was part of a project where we were using customer data to optimize marketing strategies. We identified that much of the data could be considered sensitive, as it involved personal customer behaviors and preferences. I raised my concerns about potential privacy issues with the project team and suggested that we conduct a thorough review of the data usage policies and ensure compliance with data protection regulations. To address this, we worked with the legal and compliance teams to modify our data collection and processing practices to ensure that they were transparent and secure. 

Difficulty: Basic

Leadership and project management

Question: Imagine you are leading a data science team that is working on a high-impact project with tight deadlines. Halfway through, you realize the project goals are not aligned with the latest business objectives due to changes at the executive level. How would you handle this situation to ensure the project’s success and maintain team motivation?

Sample answer: In such a scenario, my first step would be to immediately engage with stakeholders to clarify the new business objectives and gather as much information as possible about the changes at the executive level. I would then hold a meeting with my team to transparently communicate the changes and the reasons behind them, ensuring to address any concerns and gather input on how to realign our goals with the new objectives. To minimize disruption, I’d work on adjusting the project plan collaboratively, identifying which parts of our current work can be repurposed or adapted. Throughout this process, I would emphasize the importance of our adaptability as a team to new challenges, recognizing contributions already made, and motivating the team by highlighting the critical nature of our alignment with the company’s strategic goals. Regular check-ins would be scheduled to ensure the project remains on track and to provide support where needed, and I’d maintain an open dialogue to keep the team engaged and motivated.

Difficulty: Advanced

Next steps & resources

Data science is a lucrative, in-demand field that blends analytical thinking with the power to craft compelling narratives from data. While securing a data science role can be challenging—especially in today’s competitive job market—being well-prepared for the interview can significantly improve your chances. 

Whether you’re aiming for a career as a data scientist or just looking to enhance your data skills, the first step is simple and free: enroll in some CodeSignal Learn courses. You’ll be tackling real-world data problems and refining your technical skills in no time. Start your journey with CodeSignal Learn for free today and build your expertise in data science—or explore countless other technical skill areas.

The post 30 data science interview questions (and answers) from basic to senior level appeared first on CodeSignal.

• Do you know the purpose of the assessment? Know ahead of time: is this a psychometric or behavioral assessment? If so, focus on responding to questions thoughtfully and honestly. Is it a skills or knowledge assessment? In that case, you’ll want to dedicate some time to review and prepare before the assessment. 

• Have you thoroughly reviewed the job description? Job descriptions will tell you much of what you need to know about the expectations for the role you’re applying for: specific skills the team needs, tools and technologies you should know, and other characteristics that matter for the job—like communication skills, collaboration, and time management. 

• Have you researched the organization you’re applying to? This will allow you to dig deeper into the organization’s values, priorities, and culture—in addition to giving you a better understanding of the organization’s products or services, which could be an important component of your pre-hire assessment.

• Have you eliminated all distractions? You want to be “in the zone” and fully present during your assessment. It’s a good idea to close any extraneous apps and windows, silence your notifications, and let housemates know not to disturb you. Also, this might seem obvious, but low battery life has a way of sneaking up on us at the worst times — so make sure your computer is plugged in! 

• Did you get a good night’s sleep? Studies have shown that getting more than seven hours of sleep is associated with up to 10% better performance on tests. You will most likely have some flexibility around when you can take the assessment. Use that to your advantage, and choose a time when you’re feeling rested and alert.

• Are you doing something to celebrate? You put a lot of hard work into this! We’re rooting for you. Do something to relax and reward yourself when the assessment is done.  

Best of luck. You got this!

The post Getting ready for a pre-hire assessment: 6 tips appeared first on CodeSignal.

This guide is designed to demystify this journey by offering clear, actionable insights tailored specifically for early-career software engineers aiming to elevate their careers. We offer advice on which skills and work experiences you need to level up, and how to build them.

With a decade of experience helping employers find great technical talent—from junior-level, to specialized, to senior-level roles—CodeSignal knows what technical skills top companies are hiring for today. We’ve also helped hundreds of thousands of developers prepare for technical interviews and build their coding skills. This guide draws from this experience to help you hone in on the most impactful things you can do to advance from a junior to senior software engineering role.

Skip to a section

• Understanding the role
• How do I go from junior software engineer to senior?
• What technical skills are required to move from junior to senior level?
• Career advancement strategies for moving from junior to senior level
• On average, how long does it take to advance to a senior software engineer role?
• What salary range can I expect as a senior software engineer?
• Accelerate your software engineer career with CodeSignal Learn

Understanding the role

Understanding the role of a senior software engineer is the first step for those aiming to advance their career from a junior to a senior level. At the heart of this transition is an expansion of responsibility. As a senior software engineer, you’re not only expected to write clean and efficient code—you also need to take a broader view of projects to ensure that the technical solutions align with business goals. This role often involves acting as a software architect, where you design the overall structure of the system, make critical decisions about the technology stack, and solve complex problems that directly impact the project’s success.

Leadership and mentorship are other important facets of a senior software engineer’s role. Leadership is demonstrated through guiding project directions, making strategic decisions, and ensuring that the team remains aligned with the objectives of the project, team, and company. Mentorship, on the other hand, involves supporting and guiding junior engineers, sharing knowledge and best practices, and fostering a collaborative and growth-oriented team environment. Achieving proficiency in these areas requires effective communication abilities and emotional intelligence to positively influence the team’s dynamics and contribute to its success.

What is a senior software engineer?

A senior software engineer is a highly experienced professional who both contributes to the codebase and also oversees the design and architecture of software systems to ensure they align with business objectives. With a deep understanding of the entire software development lifecycle, senior software engineers lead projects, make strategic decisions, and mentor junior engineers.

What are the key responsibilities of a senior software engineer?

The key responsibilities of a senior software engineer encompass a wide range of tasks that go beyond basic coding to include system architecture design, project leadership, and the mentorship of junior staff. These engineers are pivotal in translating business requirements into technical solutions, which allows for the delivery of high-quality software that meets both user needs and company goals. Their role demands a deep understanding of technology and its practical applications, as well as the ability to guide a team towards achieving project milestones efficiently and effectively.

Some of the core responsibilities of a senior software engineering role include:

• Designing and architecting robust software systems that are scalable, efficient, and aligned with business objectives
• Writing clean, maintainable, and efficient code that follows best practices and industry standards
• Leading projects and coordinating with other departments to ensure timely and within-budget delivery
• Mentoring junior engineers, providing guidance, and fostering a collaborative and inclusive team environment
• Making strategic decisions about technology stacks and software development methodologies
• Ensuring the security, stability, and scalability of software solutions
• Conducting code reviews, enforcing quality standards, and integrating continuous improvement processes into the development lifecycle
• Staying updated with emerging technologies and industry trends to drive innovation within the organization

How do I go from junior software engineer to senior?

Going from junior software engineer to senior requires mastering advanced technical skills, understanding software architecture, and excelling in project management. Aspiring senior SWEs should embrace challenging projects, engage in continuous learning, and seek mentorship to broaden their expertise. Demonstrating reliability, innovation, and strong teamwork skills is essential for recognition and advancement to a senior role.

Educational pathways for senior software engineers

Educational pathways for aspiring senior software engineers often begin with a solid foundation in computer science, which provides the theoretical and practical knowledge essential for this advanced role. A formal education in computer science, whether through a bachelor’s degree or advanced studies, is one way individuals can equip themselves with a deep understanding of algorithms, data structures, software design, and system architecture. 

Other popular and effective options are taking a coding bootcamp or online coding courses. These are often more cost-effective than four-year degree programs, can be completed in less time, and are easier to balance with working a full-time job. The content of coding bootcamps and online courses may be more practical and job-relevant compared to some university computer science programs.

Both of these approaches to education can be helpful for developing the analytical and problem-solving skills necessary for the complex challenges faced by senior software engineers in their daily work.

Work experience

Transitioning from a junior to a senior software engineering role typically requires several years of hands-on experience, where individuals should work on a variety of projects across different stages of the software development lifecycle. This experience allows you to gain a deep understanding of coding practices, debugging, software design, and system architecture. Successful candidates for senior software engineering positions often demonstrate a track record of progressively complex responsibilities, including leading projects, mentoring peers, and making significant contributions to the architecture and scalability of software systems. 

Self-learning and online resources

Self-learning and leveraging online resources play a pivotal role in the journey from junior to senior software engineering roles. Online courses can introduce you to advanced programming concepts, software architecture principles, and the latest technological innovations. Books authored by industry experts provide in-depth knowledge and insights into best practices for senior-level responsibilities. 

Listening to podcasts is another valuable way to stay informed about emerging trends in software engineering, learn from seasoned professionals, and gain diverse perspectives on solving complex engineering problems. This combination of resources can enrich your skill set and help you land a senior-level role.

What technical skills are required to move from junior to senior level?

Advancing from a junior to a senior software engineering role demands a deep and broad set of technical skills that are critical for handling more complex and impactful projects. Proficiency in widely-used programming languages form the foundation of this skill set, providing the syntax and paradigms necessary for effective software creation. Further skills related to the software development lifecycle, algorithms and data structures, documentation and code reviews, and advanced problem-solving skills may also be required for a senior-level position.

Core programming languages

To transition from a junior to a senior software engineering role, proficiency in core programming languages is essential. However, the specific languages required can vary by company and the nature of the engineering role, ranging from web development languages like JavaScript and Python to systems programming languages such as C++ and Rust. To achieve senior-level proficiency, engineers should deepen their understanding of these languages and actively apply them in complex project scenarios. This involves engaging with advanced features, contributing to open-source projects, and/or developing substantial personal projects. 

Software development lifecycle

For engineers transitioning to a senior role, a comprehensive understanding of the Software Development Lifecycle (SDLC) is crucial. This knowledge extends beyond just coding to encompass requirements analysis, design, implementation, testing, deployment, and maintenance. Senior engineers are expected to contribute strategically at each stage to create software solutions that are both technically sound and aligned with business objectives and user needs. They should be adept at various methodologies (like Agile, Scrum, or Waterfall) and capable of guiding projects through each phase efficiently. Their big-picture perspective enables senior engineers to anticipate challenges, manage risks, and ensure the successful delivery of software projects.

Algorithms and data structures

Senior software engineers also need to have a strong grasp of algorithms and data structures. Their proficiency must go beyond the fundamentals to more advanced concepts, which allows them to solve complex problems efficiently and optimize system performance. This includes mastery over various types of data structures, from basic arrays and linked lists to more complex trees and graphs, as well as a deep understanding of sorting algorithms, search algorithms, and algorithmic complexity (Big O notation). Being proficient in these areas enables senior engineers to design and implement solutions that are scalable, efficient, and tailored to the specific needs and constraints of the project at hand.

Documentation and code reviews

Next, senior engineers must be skilled in writing comprehensive documentation and conducting thorough code reviews. They should thoroughly understand the complexities of software architecture and design patterns, as these concepts often form the basis of both documentation and code review processes. Senior engineers are expected to document complex systems clearly so that other engineers can understand the software’s architecture and maintain it over time. 

Similarly, during code reviews, senior engineers should leverage their knowledge of design patterns to evaluate the efficiency, readability, and scalability of code. Mastery in these areas ensures that senior engineers can contribute to the maintainability and quality of the codebase, which fosters a culture of collaboration and continuous improvement within their teams.

Advanced problem-solving skills

Lastly, engineers aspiring to move to a senior-level role must cultivate advanced problem-solving skills, which are fundamental to tackling the complex challenges inherent in developing sophisticated software systems. This encompasses not only a proficiency in debugging, which allows for the quick identification and resolution of issues within the code, but also a strong capacity for analytical thinking. Senior engineers are expected to approach problems methodically by breaking down large and complex issues into manageable components that can be addressed efficiently. This level of problem-solving ability enables them to devise innovative solutions and optimizations that significantly enhance the performance and reliability of software products: a key differentiator between junior and senior technical expertise.

Career advancement strategies for moving from junior to senior level

Advancing from a junior to a senior software engineering role involves a strategic blend of skill enhancement, practical experience, and professional networking. It’s important to both deepen your technical expertise and engage actively in projects that push the boundaries of your current capabilities. Two great places to start are seeking mentorship from seasoned professionals and building soft skills like communication and leadership.

Seeking mentorship and becoming a mentor

Seeking mentorship is a great first step toward becoming a senior software engineer. A mentor provides practical advice on the job search, such as refining your resume and preparing for technical interviews, as well as tips for building your skills and taking on more complex responsibilities. A good mentor may also accelerate your career growth by connecting you with new opportunities and professional networks.

Becoming a mentor yourself marks a significant step in your career progression that indicates your expertise and willingness to contribute to the development of others in the tech community. Sharing your experiences, challenges, and advice helps mentees chart their own paths more confidently. Engaging in mentorship can also build your skills in leadership and communication. This role both benefits your mentees and strengthens your own professional standing and value within your organization and the wider industry.

Cultivating soft skills

Cultivating soft skills is essential for engineers aspiring to senior roles—and often just as critical as technical expertise in advanced positions. Effective communication skills allow you to clearly articulate your ideas, project needs, and solutions to both technical and non-technical stakeholders and to foster a collaborative environment. Leadership skills will empower you to guide teams effectively, make strategic decisions, and inspire innovation, all while being a team player who promotes a positive work culture. Developing these soft skills complements your technical abilities and will make you an engineer who is well-rounded and suited for the challenges of a senior role.

On average, how long does it take to advance to a senior software engineer role?

On average, the journey from a junior to a senior software engineering role involves gaining significant experience, typically around 6 years, according to Built In. Some organizations may require more—up to 10 years, depending on the complexity of the projects and the level of expertise required​​. The progression usually starts from entry-level positions such as interns or junior software engineers, moving up to mid-level and then senior roles as the engineer acquires more technical skills and takes on management responsibilities​​.

What salary range can I expect as a senior software engineer?

As a senior software engineer, the salary range you can expect varies widely depending on factors like geographical location, industry, and company size. Recent data suggests that a senior software engineer’s salary in the United States can range from $198,000 to $329,000 per year, with some senior software engineers earning up to $412,000 per year.

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The post How to become a senior software engineer: Skills required to move from junior to senior level appeared first on CodeSignal.

Evaluating your current skill level in C++ is an important first step for effective learning. We recommend starting with basic problems and gradually progressing to more complex ones, allowing you to accurately gauge your understanding and identify areas needing improvement. Setting clear goals for yourself, like mastering specific features of C++ or preparing for a technical interview, will help you stay focused and motivated. Your goals should align with your professional development objectives and the role you’re interviewing for—whether that means becoming proficient in writing efficient code for large-scale applications, or gaining a deep understanding of lower-level C++ for system-level programming. 

To create a structured practice plan, start by allocating regular, focused practice sessions, prioritize topics based on your goals, and use a variety of resources like coding challenges, open-source projects, and community forums to enhance your learning experience. The key to mastering C++ is consistent practice and a willingness to continually challenge yourself.

Jump to a section

• What you will need to get started
• How to solve our C++ practice problems for maximum benefit
• Essential C++ practice problems (and solutions) for beginners
  • Language fundamentals and syntax
  • C++ control structures
  • Basic I/O operations with C++
  • Object-oriented programming
  • Basic data structures
• Advanced C++ practice problems (and solutions)
  • Advanced data structures and algorithms
  • Memory management
  • Concurrency and multithreading
  • Advanced features of C++
• Next steps & resources

What you will need to get started

Before diving into practice problems, you’ll need to configure your coding environment if you haven’t already. You have various options, each with its own merits and drawbacks. Trying out multiple setups can improve your adaptability, so take the time to discover which one suits your preferences and workflow best. We’ll discuss some environment options below.

IDEs

Using fully-featured IDEs for C++ interview preparation provides a comprehensive environment, integrating editing, building, and debugging seamlessly. The GUI-based project configuration simplifies the setup, making it easier to manage larger codebases efficiently. IDEs come with advanced features like code refactoring, enhancing productivity during interview simulations. However, be mindful of the potentially steep learning curve associated with complex UIs, and the resource-intensive nature of IDEs if you haven’t used one before. 

Conversely, for more senior engineers and those very comfortable with their IDE, pay attention to your debugging methods. Many interview platforms don’t have rich environments for watching variables, compilation warnings, and code completion, so consider practicing interview questions in a different environment than what you’re used to so that you’re able to adapt to a few methods of debugging.

Code editor and command line

For interview practice in C++, opting for a code editor like Sublime Text or VS Code with command-line tools provides flexibility and control. This setup allows developers to delve into the intricacies of makefiles and the build process, which can be helpful for beginners learning how compilation works in C++ and how files interact. While this setup is lightweight and fast, offering a consistent experience across platforms, be aware that configuring the toolchain and compiler may require extra effort. The lack of built-in debugging may make it difficult for beginners that are still learning C++ debugging. Nevertheless, this approach can be beneficial for honing manual coding and build skills, which are often tested in interviews.

Online IDEs

For quick, instant coding practice, online IDEs offer immediate accessibility without the need for setup. They are suitable for short, focused tests and demonstrations, making them convenient for interview preparation. This helps emulate many interview situations where you’re coding online with a less-customized environment. However, be aware of the limitations, such as restricted features compared to desktop IDEs. The inability to save work locally might be a drawback if you want to review your code later in another IDE for practice, and requiring internet access might limit when you can practice. 

Libraries and dependencies

When preparing for C++ interviews, having the right libraries accessible simplifies implementation and allows you to concentrate on core programming concepts.

Fortunately, most standard facilities needed for interview practice come built-in:

• Headers like <algorithm>, <vector>, <string> provide collections, utilities
• I/O streams using <iostream> and <fstream>
• Containers/iterators in <array>, <list>, <map>
• Multithreading primitives via <thread>, <mutex>
• Built-in smart pointers like std::unique_ptr
• These and more ship standard with any C++ compiler so are always available.

While the STL suffices for most fundamental coding challenges, some additional libraries useful to have handy include:

• Boost: For added containers, algorithms, strings, testing
• Qt: GUI development and platform abstractions
• OpenSSL” Cryptography and secure communication
• Libcurl: HTTP and network transfers

Ideally, have these pre-installed and integrated into the dev environment instead of figuring out during interviews. This removes distractions to stay focused. Most build systems or Linux package managers make acquiring these straightforward.

How to solve our C++ practice problems for maximum benefit

Beginning C++ Interview Techniques

In preparation for C++ interviews, some principles apply regardless of your seniority. Study essentials like OOP concepts of inheritance and polymorphism, handling exceptions properly with try/catch blocks, leveraging C++11 smart pointers for automatic memory management, and applying move semantics for performance. You’ll also want to become fluent with the Standard Template Library (STL), including the central vectors, maps, sets, strings, iterators, algorithms for sorting/searching, and templates for generic programming. Knowing these building blocks allows for efficiently solving most problems without reinventing built-in capabilities. Additionally, practice analyzing algorithmic complexity using big-O notation, as interviewers will often ask for the theoretical efficiency of code implementations. A solid grasp of big-O allows you to compare the scalability of different solutions critically to select the most optimal approach.

Beyond honing your proficiency in the language’s fundamentals, familiarize yourself with the compiler and build process, incorporating tools like Makefiles, as this knowledge proves invaluable for troubleshooting and optimizing code. The extent to which you’ll need to master these areas will depend on your experience and goals.

Advanced C++ Interviewing Techniques

In addition to the general advice outlined above, more senior developers will have to prepare to effectively use advanced C++ features in their interviews. When solving these more difficult problems, prioritizing optimizing speed and memory usage becomes more important as solutions can quickly become complex. As with more beginner questions, leverage existing STL containers and algorithms before opting for custom implementations, always considering space-time tradeoffs. Effective memory management is crucial—manually handle cleanup and freeing resources when working with custom allocators, and use smart pointers for automatic management. Ensure constructors and destructors are carefully managed, emphasizing clear organization for memory lifetimes and ownership intent.

When encountering questions regarding concurrency, minimize shared mutable state between threads, use mutex locks judiciously, and prefer condition variables over busy wait loops for efficiency. Make concurrent code exception-safe when needed, and stress-test to identify and address race conditions. Additionally, explore template metaprogramming (TMP) techniques, such as SFINAE (Substitution Failure Is Not An Error) and type traits, as they can empower you with tools to create flexible and efficient code. These practices contribute to a comprehensive understanding of advanced C++ concepts, enhancing your problem-solving skills and proficiency in the language.

Essential C++ practice problems (and solutions) for beginners

For introductory C++ interview questions, interview questions often prioritize evaluating problem decomposition skills, data structure comprehension, and general coding aptitude over specialized library or language syntax familiarity. That said, C++ is a difficult language, so mastering the essential language fundamentals and syntax is the initial key to success. Familiarize yourself with prevalent libraries and patterns, allowing you to concentrate on solving the interview problem without struggling with C++ details. 

With any language, you’ll need to build a solid foundation in implementing basic data structures, such as dynamic arrays and stacks, as well as common algorithm patterns and object-oriented programming principles.  This set of beginner-level and essential questions aims to evaluate your grasp on foundational concepts, setting the stage for more advanced assessments. 

Language fundamentals and syntax

Question 1: Print a custom message

Prompt: Write a C++ program that prompts the user to enter their name and then prints a greeting message, “Hello, [name]!”.

What skills this question evaluates: This question tests basic input/output operations and string manipulation in C++. It evaluates the ability to use cin for input and cout for output.

Solution:

cpp
Copy code
#include <iostream>
#include <string>
using namespace std;
int main() {
    string name;
    cout << "Enter your name: ";
    cin >> name;
    cout << "Hello, " << name << "!" << endl;
    return 0;
}

Explanation of the solution: This solution involves using the iostream library for input and output operations. The program first declares a string variable name, then uses cin to read the user’s name and cout to print the greeting. The usage of << operator for output and >> for input is fundamental in C++.

Question 2: Sum of two numbers

Prompt: Enhance your program to prompt the user to enter two integers. The program should then print the sum of these two integers.

What skills this question evaluates:  This question builds on the first by adding arithmetic operations and basic data type handling. It assesses the candidate’s ability to perform calculations and handle user input.

Solution:

cpp
Copy code
#include <iostream>
using namespace std;
int main() {
    int num1, num2;
    cout << "Enter two numbers: ";
    cin >> num1 >> num2;
    cout << "The sum is: " << num1 + num2 << endl;
    return 0;
}

Explanation of the solution: This solution introduces integer variables num1 and num2. The program uses cin to read two integers inputted by the user and calculates their sum using the + operator. This introduces the concept of arithmetic operations and multiple data input in C++. The use of cin >> num1 >> num2 demonstrates how to read multiple inputs in a single line.

C++ control structures

Question 1: Check for an even or odd number

Prompt: Write a C++ program that asks the user to input an integer and then prints whether the number is even or odd.

What skills this question evaluates: This question tests the understanding of conditional statements (if-else). It evaluates the ability to use logical expressions to make decisions in C++.

Solution:

cpp
Copy code
#include <iostream>
using namespace std;
int main() {
    int number;
    cout << "Enter an integer: ";
    cin >> number;
    if (number % 2 == 0) {
        cout << number << " is even." << endl;
    } else {
        cout << number << " is odd." << endl;
    }
    return 0;
}

Explanation of the solution: This solution uses an if-else statement to determine if a number is even or odd. The % operator is used to find the remainder when the number is divided by 2. If the remainder is 0, the number is even; otherwise, it’s odd. This demonstrates basic conditional logic in C++.

Question 2: Counting positive and negative numbers

Prompt: Modify your program to allow the user to enter multiple numbers until a zero is entered. After zero is entered, the program should print the count of positive and negative numbers entered.

What skills this question evaluates: This question builds on the previous one by adding a while loop for repeated input. It assesses the understanding of loops and conditional statements combined.

Solution:

cpp
Copy code
#include <iostream>
using namespace std;
int main() {
    int number, positiveCount = 0, negativeCount = 0;
    cout << "Enter numbers (0 to stop): ";
    while (cin >> number && number != 0) {
        if (number > 0) {
            positiveCount++;
        } else {
            negativeCount++;
        }
    }
    cout << "Positive numbers: " << positiveCount << endl;
    cout << "Negative numbers: " << negativeCount << endl;
    return 0;
}

Explanation of the solution: In this solution, a while loop is used to continuously read numbers from the user until zero is entered. Within the loop, an if-else statement increments either positiveCount or negativeCount based on whether the number is positive or negative. This question demonstrates the use of loops for repetitive tasks and conditional statements for decision-making in C++.

Basic I/O operations with C++

Question 1: User-inputted string reversal

Prompt: Write a C++ program that prompts the user to enter a string, and then prints the reverse of the string.

What skills this question evaluates: This question tests the candidate’s ability to handle input/output operations and basic string manipulation. It assesses how to use standard input to receive a string and then process it to produce the reversed version.

Solution:

cpp
#include <iostream>
#include <string>
#include <algorithm>
using namespace std;
int main() {
    string input;
    cout << "Enter a string: ";
    getline(cin, input);
    reverse(input.begin(), input.end());
    cout << "Reversed string: " << input << endl;
    return 0;
}

Explanation of the solution: The program reads a string from the user using `getline` to handle potentially spaced input. It then utilizes the `reverse` function from the standard `<algorithm>` library to reverse the string in-place. This solution demonstrates basic string handling and the use of the standard library for string manipulation.

Question 2: Writing and reading a string to/from a file

Prompt: Modify your program to write the reversed string to a file named “output.txt”. Then, read the string back from the file and print it to the console.

What skills this question evaluates: This question adds basic file handling to the previous skills of string manipulation and I/O operations. It evaluates the ability to write to and read from a file in C++.

Solution:

cpp
#include <iostream>
#include <fstream>
#include <string>
#include <algorithm>
using namespace std;
int main() {
    string input;
    cout << "Enter a string: ";
    getline(cin, input);
    reverse(input.begin(), input.end());
    // Write to file
    ofstream outFile("output.txt");
    outFile << input;
    outFile.close();
    // Read from file and print
    string fileContent;
    ifstream inFile("output.txt");
    getline(inFile, fileContent);
    cout << "String from file: " << fileContent << endl;
    inFile.close();
    return 0;
}

Explanation of the solution: This solution extends the previous string reversal program to include file I/O operations. The reversed string is written to “output.txt” using an `ofstream` object. The program then creates an `ifstream` object to read the content back from the file and prints it to the console. This demonstrates how to handle basic file operations in C++, including writing to and reading from files.

Object-oriented programming

Question 1: Creating a basic class

Prompt: Define a C++ class named `Car` with private member variables for `brand` (string), `model` (string), and `year` (integer). Include public member functions to set and get the values of these variables.

What skills this question evaluates: This question assesses understanding of basic class creation, encapsulation, and the use of public member functions for accessing private data. It focuses on the fundamental principles of object-oriented programming in C++.

Solution:

cpp
#include <iostream>
#include <string>
using namespace std;
class Car {
private:
    string brand;
    string model;
    int year;
public:
    void setBrand(const string &b) {
        brand = b;
    }
    void setModel(const string &m) {
        model = m;
    }
    void setYear(int y) {
        year = y;
    }
    string getBrand() const {
        return brand;
    }
    string getModel() const {
        return model;
    }
    int getYear() const {
        return year;
    }
};
int main() {
    Car myCar;
    myCar.setBrand("Toyota");
    myCar.setModel("Corolla");
    myCar.setYear(2020);
    cout << "Car Details: " << myCar.getBrand() << " " << myCar.getModel() << " " << myCar.getYear() << endl;
    return 0;
}

Explanation of the solution: The `Car` class is defined with private member variables (`brand`, `model`, `year`) and public methods (`setBrand`, `setModel`, `setYear`, `getBrand`, `getModel`, `getYear`) for setting and getting these variables. This encapsulates the data and provides controlled access to it. The `main` function demonstrates creating an instance of `Car` and using its methods.

Question 2: Inheritance and polymorphism

Prompt: Extend the `Car` class by creating a subclass named `ElectricCar` that includes an additional private member variable for `batteryRange` (integer) and corresponding public functions. Demonstrate polymorphism by creating a function that takes a `Car` object and prints its details.

This question tests understanding of inheritance and polymorphism. It evaluates the ability to extend a base class and use polymorphic behavior to work with objects of both the base and derived classes.

Solution:

cpp
#include <iostream>
#include <string>
using namespace std;
class Car {
    // ... (same as above)
};
class ElectricCar : public Car {
private:
    int batteryRange;
public:
    void setBatteryRange(int range) {
        batteryRange = range;
    }
    int getBatteryRange() const {
        return batteryRange;
    }
};
void printCarDetails(const Car &car) {
    cout << "Car Details: " << car.getBrand() << " " << car.getModel() << " " << car.getYear() << endl;
}
int main() {
    ElectricCar myElectricCar;
    myElectricCar.setBrand("Tesla");
    myElectricCar.setModel("Model S");
    myElectricCar.setYear(2022);
    myElectricCar.setBatteryRange(400);
    printCarDetails(myElectricCar);
    return 0;
}

Explanation of the solution: The `ElectricCar` class is a subclass of `Car` and includes an additional property `batteryRange`. The `printCarDetails` function takes a `Car` object as a parameter, demonstrating polymorphism by also accepting objects of the `ElectricCar` subclass. In `main`, an `ElectricCar` object is created, its properties are set, and it is passed to `printCarDetails`. This showcases inheritance by extending the `Car` class and polymorphism through the use of a function accepting objects of both the base and derived class.

Basic data structures

Question 1: Implementing a dynamic array

Prompt: Write a C++ class `DynamicArray` that mimics the behavior of a dynamically resizable array. It should start with a capacity of 1 and double in size when needed. Implement methods `add(int item)` to add an item to the array and `get(int index)` to retrieve an item by index.

What skills this question evaluates: This question assesses understanding of dynamic memory allocation and array resizing, fundamental concepts in managing data structures. It tests the candidate’s ability to implement basic operations like adding items and accessing them.

Solution:

cpp
#include <iostream>
#include <cassert>
using namespace std;
class DynamicArray {
private:
    int* data;
    int size;
    int capacity;
    void resize() {
        capacity *= 2;
        int* newData = new int[capacity];
        for (int i = 0; i < size; i++) {
            newData[i] = data[i];
        }
        delete[] data;
        data = newData;
    }
public:
    DynamicArray() : size(0), capacity(1), data(new int[capacity]) {}
    ~DynamicArray() {
        delete[] data;
    }
    void add(int item) {
        if (size == capacity) {
            resize();
        }
        data[size++] = item;
    }
    int get(int index) {
        assert(index >= 0 && index < size);
        return data[index];
    }
};
int main() {
    DynamicArray arr;
    arr.add(5);
    arr.add(10);
    cout << "Element at index 0: " << arr.get(0) << endl;
    cout << "Element at index 1: " << arr.get(1) << endl;
    return 0;
}

Explanation of the solution: The `DynamicArray` class uses a dynamic array `data` to store integers. It automatically resizes (doubles its capacity) when needed. The `add` method inserts an item, and `get` retrieves an item at a given index, with an `assert` statement ensuring the index is valid. The constructor initializes the array, and the destructor releases the allocated memory.

Question 2: Implementing a stack

Prompt: Based on your `DynamicArray` class, implement a class `Stack` that supports push, pop, and top operations. Ensure that `pop` and `top` handle the case when the stack is empty.

What skills this question evaluates: This question tests the ability to use existing data structures (like the dynamic array) to implement another data structure (stack). It evaluates understanding of stack operations and error handling in C++.

Solution:

cpp
#include <iostream>
#include <cassert>
using namespace std;
class Stack {
private:
    DynamicArray arr;
public:
    void push(int item) {
        arr.add(item);
    }
    int pop() {
        assert(!isEmpty());
        return arr.get(--arr.size);
    }
    int top() {
        assert(!isEmpty());
        return arr.get(arr.size - 1);
    }
    bool isEmpty() {
        return arr.size == 0;
    }
};
int main() {
    Stack stack;
    stack.push(10);
    stack.push(20);
    cout << "Top element: " << stack.top() << endl;
    cout << "Popped element: " << stack.pop() << endl;
    cout << "New top element: " << stack.top() << endl;
    return 0;
}

Explanation of the solution: The `Stack` class uses an instance of `DynamicArray` to manage its elements. The `push` operation adds an element to the stack, while `pop` removes and returns the top element. The `top` operation returns the top element without removing it. Both `pop` and `top` include assertions to check that the stack is not empty before attempting to access elements. This solution demonstrates how to build a stack using an underlying dynamic array structure and includes basic error handling.

Advanced C++ practice problems (and solutions)

C++ is a complex language, and it requires time and effort to master. While you may be well-versed in some advanced concepts as a senior programmer, others may be less familiar to you. Strengthen your expertise in the areas you know thoroughly to demonstrate your skills and proficiency. Additionally, build a foundation in other concepts to highlight your versatility and adaptability. 

As a senior C++ developer, the expectations go beyond writing functional code; comprehensive knowledge of software design principles becomes crucial. Senior-level interviews delve into more complex paradigms like memory management, multithreading, and optimization strategies, emphasizing scalable and maintainable solutions. Use the following practice problems to recognize your areas of strength. After completing them, work on improving your code and exploring alternate solutions to problems so that you are prepared for a variety of challenges.

Advanced data structures and algorithms

Question 1: Implementing a Binary Search Tree (BST)

Prompt: Write a C++ class `BinarySearchTree` that implements a basic binary search tree. The class should have methods to insert a new key, `insert(int key)`, and to check if a key exists in the tree, `search(int key)`.

What skills this question evaluates: This question assesses understanding of binary search trees, a fundamental data structure in computer science. It evaluates the ability to implement key operations like insertion and search, which require recursive thinking and an understanding of tree traversal.

Solution:

cpp
#include <iostream>
using namespace std;
class Node {
public:
    int key;
    Node *left, *right;
    Node(int item) : key(item), left(nullptr), right(nullptr) {}
};
class BinarySearchTree {
private:
    Node* root;
    Node* insertRec(Node* root, int key) {
        if (root == nullptr) {
            return new Node(key);
        }
        if (key < root->key) {
            root->left = insertRec(root->left, key);
        } else {
            root->right = insertRec(root->right, key);
        }
        return root;
    }
    bool searchRec(Node* root, int key) {
        if (root == nullptr) {
            return false;
        }
        if (root->key == key) {
            return true;
        }
        return key < root->key ? searchRec(root->left, key) : searchRec(root->right, key);
    }
public:
    BinarySearchTree() : root(nullptr) {}
    void insert(int key) {
        root = insertRec(root, key);
    }
    bool search(int key) {
        return searchRec(root, key);
    }
};
int main() {
    BinarySearchTree bst;
    bst.insert(10);
    bst.insert(5);
    bst.insert(15);
    cout << "Is 10 in BST? " << (bst.search(10) ? "Yes" : "No") << endl;
    cout << "Is 20 in BST? " << (bst.search(20) ? "Yes" : "No") << endl;
    return 0;
}

Explanation of the solution: The `BinarySearchTree` class uses a nested `Node` class to represent each node in the tree. The `insert` method adds a new key to the tree, while `search` checks for the existence of a key. Both methods use private recursive helper functions (`insertRec`, `searchRec`) to traverse the tree.

Question 2: Depth-First Search (DFS) in a graph

Prompt: Implement a graph represented as an adjacency list and perform a depth-first search (DFS) from a given starting node. Write a function `DFS(int startNode)` that prints the nodes visited during the search.

What skills this question evaluates: This question tests understanding of graph data structures and depth-first search, an important algorithm in graph theory. It assesses the ability to implement graphs and traverse them using recursive or stack-based approaches.

Solution:

cpp
#include <iostream>
#include <list>
#include <vector>
using namespace std;
class Graph {
private:
    int numVertices;
    list<int> *adjLists;
    vector<bool> visited;
    void DFSUtil(int vertex) {
        visited[vertex] = true;
        cout << vertex << " ";
        for (int adj : adjLists[vertex]) {
            if (!visited[adj]) {
                DFSUtil(adj);
            }
        }
    }
public:
    Graph(int vertices) : numVertices(vertices), adjLists(new list<int>[vertices]), visited(vertices, false) {}
    void addEdge(int src, int dest) {
        adjLists[src].push_back(dest);
    }
    void DFS(int startNode) {
        fill(visited.begin(), visited.end(), false);
        DFSUtil(startNode);
    }
};
int main() {
    Graph g(4);
    g.addEdge(0, 1);
    g.addEdge(0, 2);
    g.addEdge(1, 2);
    g.addEdge(2, 0);
    g.addEdge(2, 3);
    g.addEdge(3, 3);
    cout << "Depth First Traversal starting from vertex 2:" << endl;
    g.DFS(2);
    return 0;
}

Explanation of the solution: The `Graph` class uses an adjacency list for representing the graph and a `visited` vector to track visited nodes. The `addEdge` method adds edges to the graph. The `DFS` method initializes the `visited` vector and calls `DFSUtil`, a private method that performs recursive depth-first traversal from the specified node, printing each visited node. This solution demonstrates the implementation of a graph and a fundamental graph traversal algorithm.

Memory management

Question 1: Implementing a smart pointer

Prompt: Write a template class `SmartPointer` that mimics the behavior of a smart pointer. The class should be able to hold a pointer of any type and should release the memory when it is no longer in use (i.e., implement basic reference counting).

What skills this question evaluates: This question assesses the understanding of dynamic memory management and the concept of smart pointers in C++, particularly the implementation of reference counting to manage memory automatically.

Solution:

cpp
#include <iostream>
using namespace std;
template <typename T>
class SmartPointer {
private:
    T* ptr;
    unsigned* count;
public:
    SmartPointer(T* p = nullptr) : ptr(p), count(new unsigned(1)) {}
    SmartPointer(const SmartPointer<T>& sp) : ptr(sp.ptr), count(sp.count) {
        (*count)++;
    }
    SmartPointer<T>& operator=(const SmartPointer<T>& sp) {
        if (this != &sp) {
            if (--(*count) == 0) {
                delete ptr;
                delete count;
            }
            ptr = sp.ptr;
            count = sp.count;
            (*count)++;
        }
        return *this;
    }
    ~SmartPointer() {
        if (--(*count) == 0) {
            delete ptr;
            delete count;
        }
    }
    T& operator*() {
        return *ptr;
    }
};
int main() {
    SmartPointer<int> sp1(new int(10));
    SmartPointer<int> sp2 = sp1;
    cout << "Value: " << *sp2 << endl;
    return 0;
}

Explanation of the solution: The `SmartPointer` template class handles a pointer and a reference count. The constructor initializes the pointer and the reference count. The copy constructor and copy assignment operator manage the reference count, increasing it when a new reference is created and decreasing it when a reference is destroyed or changed. If the count reaches zero, the memory is released. This ensures that memory is automatically managed and freed when no longer needed, demonstrating basic principles of smart pointers.

Question 2: Custom memory allocator

Prompt: Create a custom memory allocator class `CustomAllocator` that allocates memory for an array of a specified type but does not construct the objects. Implement methods `allocate(size_t n)` for allocating memory and `deallocate()` for deallocating memory.

What skills this question evaluates: This question tests the candidate’s understanding of lower-level memory management in C++, particularly the concepts of memory allocation and deallocation without object construction and destruction.

Solution:

cpp
#include <iostream>
using namespace std;
template <typename T>
class CustomAllocator {
private:
    T* array;
public:
    CustomAllocator() : array(nullptr) {}
    T* allocate(size_t n) {
        array = static_cast<T*>(operator new[](n * sizeof(T)));
        return array;
    }
    void deallocate() {
        operator delete[](array);
    }
};
int main() {
    CustomAllocator<int> allocator;
    int* arr = allocator.allocate(5);
    // Use the allocated array (construct objects if necessary)
    for (int i = 0; i < 5; ++i) {
        new (&arr[i]) int(i);  // Placement new
    }
    // Manually call destructor for constructed objects
    for (int i = 0; i < 5; ++i) {
        arr[i].~int();
    }
    allocator.deallocate();
    return 0;
}

Explanation of the solution: The `CustomAllocator` template class handles raw memory allocation and deallocation for an array of type `T`. The `allocate` method uses `operator new[]` to allocate unconstructed memory and returns a pointer to this memory. The `deallocate` method frees the memory using `operator delete[]`. In `main`, the allocated memory is used to manually construct and destruct objects using placement new and explicit destructor calls, demonstrating an understanding of memory allocation without automatic construction and destruction. This example showcases a more advanced and controlled approach to memory management in C++.

Concurrency and multithreading

Question 1: Implementing a thread-safe queue

Prompt: Create a C++ class `ThreadSafeQueue` that implements a thread-safe queue using mutexes. The class should provide `enqueue` and `dequeue` methods to add and remove items, ensuring thread safety.

What skills this question evaluates: This question tests the candidate’s understanding of thread synchronization in C++ using mutexes. It evaluates the ability to implement basic thread-safe data structures, crucial in multithreaded applications.

Solution:

cpp
#include <iostream>
#include <queue>
#include <mutex>
#include <condition_variable>
using namespace std;
template <typename T>
class ThreadSafeQueue {
private:
    queue<T> queue;
    mutex mtx;
    condition_variable cv;
public:
    void enqueue(T item) {
        lock_guard<mutex> lock(mtx);
        queue.push(item);
        cv.notify_one();
    }
    T dequeue() {
        unique_lock<mutex> lock(mtx);
        cv.wait(lock, [this] { return !queue.empty(); });
        T item = queue.front();
        queue.pop();
        return item;
    }
};
int main() {
    ThreadSafeQueue<int> tsQueue;
    // Example usage: tsQueue.enqueue(10);
    // Example usage: int item = tsQueue.dequeue();
    return 0;
}

Explanation of the solution: The `ThreadSafeQueue` class uses a standard queue and a mutex for synchronization. The `enqueue` method locks the mutex, adds an item to the queue, and then notifies one waiting thread. The `dequeue` method waits (without busy-waiting) until the queue is not empty and then removes an item from the queue. This implementation ensures thread safety for enqueueing and dequeueing operations, demonstrating key concepts in concurrent C++ programming.

Question 2: Implementing a simple thread pool

Prompt: Create a C++ class `ThreadPool` that manages a fixed number of threads. The class should be able to execute tasks (function objects) added to a queue. Implement methods to add tasks and to shut down the thread pool gracefully.

What skills this question evaluates: This question assesses advanced understanding of multithreading, specifically the management of multiple threads and task execution. It evaluates the ability to implement a thread pool and manage its lifecycle.

Solution:

cpp
#include <iostream>
#include <vector>
#include <thread>
#include <functional>
#include <mutex>
#include <condition_variable>
#include <queue>
using namespace std;
class ThreadPool {
private:
    vector<thread> workers;
    queue<function<void()>> tasks;
    mutex mtx;
    condition_variable cv;
    bool stop;
public:
    ThreadPool(size_t threads) : stop(false) {
        for (size_t i = 0; i < threads; ++i) {
            workers.emplace_back([this] {
                while (true) {
                    function<void()> task;
                    {
                        unique_lock<mutex> lock(this->mtx);
                        this->cv.wait(lock, [this] { return this->stop || !this->tasks.empty(); });
                        if (this->stop && this->tasks.empty())
                            return;
                        task = this->tasks.front();
                        this->tasks.pop();
                    }
                    task();
                }
            });
        }
    }
    void enqueue(function<void()> task) {
        {
            lock_guard<mutex> lock(mtx);
            tasks.push(task);
        }
        cv.notify_one();
    }
    void shutdown() {
        {
            lock_guard<mutex> lock(mtx);
            stop = true;
        }
        cv.notify_all();
        for (thread &worker : workers) {
            worker.join();
        }
    }
    ~ThreadPool() {
        if (!stop) {
            shutdown();
        }
    }
};
int main() {
    ThreadPool pool(4);
    // Example usage: pool.enqueue([]{ cout << "Task executed." << endl; });
    return 0;
}

Explanation of the solution: The `ThreadPool` class manages a fixed number of worker threads and a task queue. Worker threads execute tasks from the queue. Tasks are function objects enqueued using the `enqueue` method. The `shutdown` method stops all threads after completing any remaining tasks. The use of mutexes and condition variables ensures thread-safe access to the task queue and coordinated execution. This solution demonstrates a fundamental pattern in concurrent programming: managing a pool of threads to efficiently execute tasks.

Advanced features of C++

Question 1: Implementing a custom template metaprogram

Prompt: Write a C++ template metaprogram `Factorial` that computes the factorial of a compile-time constant integer. Use template specialization to achieve this.

What skills this question evaluates: : This question assesses advanced knowledge of C++ template metaprogramming, an area that involves using templates to perform computations at compile time. It evaluates the ability to use recursive template instantiations and template specialization.

Solution:

cpp
#include <iostream>
template <unsigned int N>
struct Factorial {
    static const unsigned int value = N * Factorial<N - 1>::value;
};
template <>
struct Factorial<0> {
    static const unsigned int value = 1;
};
int main() {
    std::cout << "Factorial of 5: " << Factorial<5>::value << std::endl;
    return 0;
}

Explanation of the solution: The `Factorial` template struct calculates the factorial of a number at compile time. It uses recursive template instantiation, with each instance calculating part of the factorial. The specialized version of the template for the base case `Factorial<0>` provides the stopping condition for the recursion. This is a classic example of template metaprogramming, leveraging C++ templates’ powerful ability for compile-time computation.

Question 2: Utilizing advanced C++17 features

Prompt: Using C++17 features, write a function `processVariants` that takes a `std::variant` of different types (e.g., `int`, `double`, `std::string`) and uses a `std::visit` to apply a lambda function that prints the value regardless of its type.

What skills this question evaluates:  This question tests understanding of newer C++17 features, particularly `std::variant` and `std::visit`. It assesses the ability to work with type-safe unions and visitation patterns, showcasing proficiency in modern C++ idioms.

Solution:

cpp
#include <iostream>
#include <variant>
#include <string>
using VariantType = std::variant<int, double, std::string>;
void processVariants(const VariantType& var) {
    std::visit([](const auto& value) {
        std::cout << value << std::endl;
    }, var);
}
int main() {
    VariantType var1 = 10;
    VariantType var2 = 3.14;
    VariantType var3 = "Hello C++17";
    processVariants(var1);
    processVariants(var2);
    processVariants(var3);
    return 0;
}

Explanation of the solution: The function `processVariants` takes a `std::variant` and uses `std::visit` along with a generic lambda to process and print the contained value. The lambda uses auto type deduction to work with any type contained in the variant. This example demonstrates how C++17 introduces more flexible and type-safe ways to handle a set of types, streamlining what would otherwise require more complex and less safe approaches.

Next steps & resources

Getting better at C++ interviewing requires patience and analytical thinking more than just coding prowess. Understand each question deeply before starting to code, and think through use cases thoroughly. Consider working out the logic on paper before writing so you have a clear idea of what you’re trying to accomplish with the code. Once the solution is complete, refine it to produce clean, well-commented code, to facilitate ease in future writing. Thoroughly test the output, dedicating time to explore potential edge cases to guarantee the completeness of your solution.

By taking these steps with the practice questions above (and with more specialized applications of C++ for game development, machine learning, and others) you’ll set yourself up to cultivate a robust understanding of the fundamentals through consistent practice, rather than trying to “cram” concepts shortly before an interview. Practicing interview scenarios, either solo or with a peer, can also help you build confidence and improve ability to problem-solve on the spot.

CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Our Fundamental Coding Interview Preparation with C++ learning path was designed to help you prepare for coding assessments and technical interviews in C++ using realistic practice exercises. Sign up to get started for free.

The post Key C++ practice problems (and solutions) from beginner to senior level appeared first on CodeSignal.

To prepare efficiently, it’s important for developers to accurately evaluate their own skills in Python and technical interviewing. This guide helps in this assessment by categorizing questions by difficulty. Beginners can gauge their understanding of basics, while advanced programmers can test their knowledge of more complex use cases. Recognizing your proficiency level helps you focus your preparation on areas that require the most attention.

It’s important to set specific goals for your Python interview preparation based on your skill and type of role you’re interviewing for. This guide will help you identify key areas for improvement, such as data structures, object-oriented programming, or library specific knowledge. Once you set your goals, you can create a focused and tailored practice plan that includes regular coding exercises and mock interview scenarios. 

Let’s get started.

Jump to a section:

• What you will need to get started
• How to solve our Python interview questions
• Basic Python interview questions
• Intermediate to advanced Python interview practice questions
  • Python algorithms interview questions
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• Python full-stack engineer interview questions
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• Next steps & resources

What you will need to get started

For day-to-day coding, developers often rely on fully-featured Integrated Development Environments (IDEs) such as PyCharm, leveraging tools like debugging, auto-complete, and code navigation. However, interview coding environments are generally more lightweight, intentionally limiting available features to concentrate on assessing coding abilities. Some may only allow debugging using print statements. We’ve observed that developers accustomed to the rich debugging capabilities of IDEs can sometimes encounter challenges when transitioning to these constrained coding environments.

Therefore, while full IDEs prove ideal for regular development, we strongly recommend practicing coding interviews using simpler text editors that mirror the conditions of actual interview coding platforms. Those who intentionally practice in environments resembling interviews tend to feel more at ease. If you opt for practicing in a feature-rich IDE, consider refraining from using advanced features like variable watching and instead focus on debugging using print statements. If your interview IDE does offer extra features, view them as an added bonus.

Similarly, unless an interview explicitly evaluates proficiency with a framework like NumPy or TensorFlow, minimize the use of external packages and imports. Interview questions typically center around base language skills and standard library functionality. If you are accustomed to heavily relying on packages, explore alternative ways of implementing their capabilities.

How to solve our Python interview questions

• Review computer science fundamentals: Familiarize yourself with basic data types (strings, lists, dictionaries) and control flow statements. Ensure comfort with the structure and syntax of Python code. Brush up on time and space complexity so you can evaluate those for all problems. 
• Practice common algorithm questions: Code solutions to standard algorithm questions like fizzbuzz, reversing a string, and the Fibonacci sequence. Many coding questions tend to build off the basics, and knowing some common patterns will make approaching new problems easier.
• Master built-in Python features: Become adept at using built-in Python data structures and methods. Understand methods for lists, dictionaries, sets, strings, etc., and know when to apply them.
• Handle input/output: Review input/output operations in Python, including reading input, opening files, and writing output. Develop proficiency in debugging with print statements.
• Communication is key: Practice speaking through your code as you would during the interview; articulate the purpose of each line. After writing a solution, generate test cases after coding, considering base cases, edge cases, and invalid input.
• Coding style matters: Embrace good coding style with meaningful variable names, proper indentations, and spaces for readability. Add comments where they enhance understanding. Your code should be clear enough for an interviewer to read and understand your thought process. Practice writing clear code early so it’s second nature during your interview.
• Problem-solving approach: If you encounter difficulties, think aloud about how to break down the problem and try to solve smaller subproblems first. During the actual interview, you will have the interviewer to lean on—but if you’re practicing by yourself, you’ll have to work out ways to guide yourself. Consider using paper to write down ideas, write out test cases, and work out the logic step-by-step before coding.

Tips for basic level Python interview coding questions

For junior-level positions, interviewers aim to evaluate your aptitude for learning, problem-solving, and grasp of fundamental computer science concepts. While the assessment may not delve deeply into Python domain knowledge, being fluent in the language significantly enhances your coding speed and enables you to concentrate on solving the given problem effectively. When tackling a problem, prioritize developing a working solution initially, and then explore optimization possibilities. Recognize that problems often have multiple viable solutions, and experimenting with different approaches can be beneficial. At this early stage in your coding journey, gaining more practice proves to be the most advantageous strategy.

Interview prep tip: The Fundamental Coding Interview Preparation with Python learning path in CodeSignal Learn is designed to help you prepare for coding assessments and technical interviews for early-career developer roles.

This series of 5 courses takes you through the fundamentals of Python syntax, algorithms, data structures, and problem-solving so you can ace your next interview—and it’s free to get started.

Tips for senior level Python interview coding questions

For senior-level Python interviews, anticipate a variety of challenges that extend beyond coding proficiency. As you become more specialized, the requirements for domain knowledge are likely to increase beyond fundamental understanding.  After spending significant time in the industry, preparing for the more pedagogical problems that often arise in interviews can feel both challenging and unfamiliar. Consider initially practicing with beginner-level problems to reacquaint yourself with the essential skills required for interviews before delving into more domain-specific scenarios. When you transition to advanced topics, approach them with a fresh perspective, acknowledging that your prior experiences may differ from the canonical questions often posed in interviews.

Interview prep tip: Mastering Algorithms and Data Structures in Python is a learning path in CodeSignal Learn designed to help you prepare for technical interviews for mid- to senior-level SWE roles requiring skills in Python.

Basic Python interview questions

For questions at a beginner level, interviewers may want to evaluate your computer science fundamentals more than they want to see deeper knowledge of Python functions and capabilities. They may ask you not to use built-in functions and ask you to build them from scratch, to show that you understand how these functions work. Other times, they may expect you to demonstrate enough knowledge of the language to know when to use them. For interview practice, try writing multiple solutions for each problem you encounter. If you’re unsure what to use during the actual interview, ask your interviewer for clarification.

Question 1: Adding up elements in a list

Prompt: Write a Python function that takes a list of numbers and returns the sum of all elements in the list. For example, for the list [1, 2, 3, 4], the function should return 10. 

What this question evaluates: This question assesses basic list handling and the use of loops in Python. For this reason, don’t use the built-in Python sum function in your initial implementation.

Solution:

def sum_of_list(numbers):
    total = 0
    for number in numbers:
        total += number
    return total

Explanation of solution: The function iterates over each element in the list numbers using a for loop. It initializes a variable total to 0 and adds each element of the list to this variable, accumulating the sum. Finally, it returns the total sum of the elements.

Question 2: Finding the highest number in a list

Prompt: Build on your previous function to return the largest number in the list, in addition to the sum. For the list [1, 2, 3, 4], the function should return the sum 10 and the maximum number 4.

What this question evaluates: This question builds on the first question by adding an understanding of how to compare elements in a list. For this problem, don’t use the built-in max function.

Solution:

def sum_and_max_of_list(numbers):
    total = 0
    max_number = numbers[0]  # Assume the first number is the largest initially
    for number in numbers:
        total += number
        if number > max_number:
            max_number = number
    return total, max_number

Explanation of solution: The function initializes two variables: total to store the sum and max_number to store the current maximum number, initially set to the first element in the list. As it iterates through the list, it adds each element to total. Simultaneously, it checks if each element is greater than the current max_number. If so, it updates max_number. It returns both the total sum and the maximum number found in the list.

Question 3: Counting occurrences of a specific element in a list

Prompt: Write a function that takes a list and a target number, and returns the count of occurrences of the target number in the list. For instance, in the list [1, 2, 3, 2, 2, 4] and target number 2, the function should return 3.

What this question evaluates: This question tests basic list operations and conditional logic in loops. Avoid using the count function at this time in order to practice the underlying technique.

Solution:

def count_occurrences(numbers, target):
count = 0
    for number in numbers:
        if number == target:
            count += 1
    return count

Explanation of solution: The function iterates over the list numbers. It uses a variable count to keep track of how many times the target number appears in the list. Each time it finds the target number, it increments count. After iterating through the list, it returns the total count of the target number’s occurrences.

Intermediate to advanced Python interview practice questions

In tackling intermediate-level questions and beyond, the emphasis pivots toward evaluating advanced problem-solving proficiency and a more profound familiarity with intricate coding concepts.  A deeper understanding of Python becomes not just beneficial but increasingly necessary, as you’ll be expected to have sufficient knowledge about the inner workings of the implementation of Python’s built-in methods and data structures. The expectation extends beyond simply problem-solving to an understanding of efficient solutions and the strategic trade-offs involving time and space in your implementations. Successful preparation for interviews at this level involves practicing a diverse set of challenging coding exercises, mastering more complex algorithms, and deepening your understanding of Python’s libraries and data structures.

Python algorithms interview questions

Question 1: Reversing a string

Prompt: Write a Python function to reverse a given string. For example, if the input string is “hello”, the output should be “olleh”.

What this question evaluates: This question assesses basic understanding of string manipulation and iteration in Python.

Solution:

def reverse_string(s):
    return s[::-1]

Explanation of solution: The solution uses Python’s slicing mechanism. The slice [::-1] is a common Python idiom for reversing a string (or a list). It starts from the end towards the first character, stepping backwards. s[::-1] takes the entire string s and reverses it.

Question 2: Checking for a palindrome

Prompt: Enhance the previous function to check if the given string is a palindrome. A palindrome is a word that reads the same backward as forward, e.g., “radar”.

What this question evaluates: This question builds on the first question by adding conditional logic and understanding of string properties.

Solution:

def is_palindrome(s):
reversed_s = s[::-1]
    return s == reversed_s

Explanation of solution: This solution first reverses the input string using the slicing method s[::-1]. It then compares the original string s with the reversed string. If they are identical, it means the string is a palindrome.

Question 3: Counting palindromic substrings

Prompt: Write a function to count the number of palindromic substrings in a given string. For instance, in the string “aba”, there are three palindromic substrings: “a”, “b”, “aba”.

What this question evaluates: This question tests more advanced algorithmic thinking that involves string manipulation, nested loops, and understanding of substrings.

Solution:

def count_palindromic_substrings(s):
count = 0
    for i in range(len(s)):
        for j in range(i, len(s)):
            if s[i:j+1] == s[i:j+1][::-1]:
                count += 1
    return count

Explanation of solution: The function uses nested loops to generate all possible substrings of the input string. The outer loop fixes the starting point of the substring, and the inner loop varies the endpoint. For each substring generated (s[i:j+1]), the function checks if it is a palindrome (by comparing it to its reverse). The count is incremented each time a palindromic substring is found.

Python data structures interview questions

Question 1: Implementing a stack

Prompt: Implement a stack data structure in Python using lists. Your stack should support push, pop, and peek operations.

What this question evaluates: This question assesses the understanding of basic data structures (like stacks) and methods to manipulate them using Python lists.

Solution: 

class Stack:
def __init__(self):
        self.items = []
    def push(self, item):
        self.items.append(item)
    def pop(self):
        return self.items.pop()
    def peek(self):
        return self.items[-1] if self.items else None
    def is_empty(self):
        return len(self.items) == 0

Explanation of solution: The Stack class uses a Python list to store elements. push adds an item to the end of the list, pop removes the last item, and peek returns the last item without removing it. is_empty checks whether the stack is empty, which is crucial for the subsequent questions.

Question 2: Creating a queue using 2 stacks

Prompt: Using your stack implementation from Question 1, create a queue data structure. Implement enqueue and dequeue operations using two instances of your stack.

What this question evaluates: This question builds upon the stack implementation to create a more complex data structure (queue) using two stacks. This tests the understanding of how different data structures can be combined and the efficiency of operations.

Solution:

class Queue:
def __init__(self):
        self.in_stack = Stack()
        self.out_stack = Stack()
    def enqueue(self, item):
        self.in_stack.push(item)
    def dequeue(self):
        if self.out_stack.is_empty():
            while not self.in_stack.is_empty():
                self.out_stack.push(self.in_stack.pop())
        return self.out_stack.pop()

Explanation of solution: The Queue class uses two instances of the Stack class. One stack (in_stack) is used for enqueue operations, and the other (out_stack) for dequeue operations. For dequeue, if out_stack is empty, all elements from in_stack are popped and pushed into out_stack. This reverses the order of elements, making the earliest enqueued element available for dequeue.

Question 3: Make a balanced parentheses checker

Prompt: Write a function that uses your stack implementation to check if a string of parentheses (e.g., ‘((()))’, ‘()()’) is balanced. Every opening parenthesis must have a corresponding closing parenthesis.

What this question evaluates: This question requires using the stack to solve a common programming problem, testing knowledge of both data structures and algorithms, as well as string processing.

Solution:

def is_balanced_parentheses(string):
stack = Stack()
    for char in string:
        if char == '(':
            stack.push(char)
        elif char == ')':
            if stack.is_empty():
                return False
            stack.pop()
    return stack.is_empty()

Explanation of solution: This function iterates through each character in the input string. If an opening parenthesis is encountered, it is pushed onto the stack. For a closing parenthesis, the function checks if the stack is empty (unmatched closing parenthesis) or pops from the stack (matching pair found). At the end, if the stack is empty, all parentheses are balanced; otherwise, they are not.

Python automation testing interview questions

Question 1: Writing a basic test case using unittest

Prompt: Write a Python test case using the unittest framework to test a function add(a, b) that returns the sum of two numbers. Include both a passing and a failing test.

What this question evaluates: This question assesses the basic understanding of the unittest framework, one of Python’s standard libraries for testing. It evaluates the ability to write simple test cases and understand test results.

Solution:

import unittest
def add(a, b):
    return a + b
class TestAddFunction(unittest.TestCase):
    def test_addition(self):
        self.assertEqual(add(1, 2), 3)
    def test_failed_addition(self):
        self.assertNotEqual(add(1, 2), 4)
if __name__ == '__main__':
    unittest.main()

Explanation of solution: The test class TestAddFunction extends unittest.TestCase. Two test methods are defined: test_addition (a passing test) and test_failed_addition (a failing test), using assertEqual and assertNotEqual to verify the function’s output.

Question 2: Implementing mocking in a test case

Prompt: Suppose you have a function fetch_data(api_url) that retrieves data from an API. Write a test case using unittest.mock to mock the API call, ensuring it does not make an actual HTTP request. Test that the function returns a predefined response.

What this question evaluates: This tests the candidate’s knowledge of mocking in Python, a crucial technique for testing code that interacts with external services or dependencies. It evaluates the ability to mock external calls to isolate tests from their external dependencies, allowing you to precisely control the inputs and outputs to validate the code handles various scenarios and edge cases

Solution:

import unittest
from unittest.mock import patch
def fetch_data(api_url):
    # Function that makes an HTTP request to the provided api_url
    pass
class TestFetchData(unittest.TestCase):
    @patch('path.to.fetch_data_function')
    def test_mock_api_call(self, mock_fetch):
        mock_fetch.return_value = "Mocked Data"
        response = fetch_data("http://example.com/api")
        self.assertEqual(response, "Mocked Data")
if __name__ == '__main__':
    unittest.main()

Explanation of solution: The unittest.mock module is used to replace the fetch_data function with a mock during the test. @patch decorator is applied to mock the function. mock_fetch.return_value sets a predefined return value for the mock. The test verifies that fetch_data returns the mocked response instead of performing a real API call.

Question 3: Testing asynchronous code

Prompt: Write a test case for an asynchronous function async fetch_data(api_url) that retrieves data from an API. Ensure the test properly waits for the function to complete and checks the returned result.

What this question evaluates: This question focuses on testing asynchronous Python code, a key skill in modern Python development. It assesses understanding of async features in Python and the ability to write tests for async functions.

Solution:

import asyncio
import unittest
async def fetch_data(api_url):
    # Asynchronous function to fetch data
    pass
class TestAsyncFetchData(unittest.TestCase):
    def test_async_fetch_data(self):
        loop = asyncio.get_event_loop()
        response = loop.run_until_complete(fetch_data("http://example.com/api"))
        self.assertEqual(response, expected_response)
if __name__ == '__main__':
    unittest.main()

Explanation of solution: This solution involves testing an asynchronous function fetch_data. An event loop is obtained using asyncio.get_event_loop(). loop.run_until_complete() is used to run the asynchronous function within the test, ensuring the test waits for its completion. The result of the async function is then tested using assertEqual.

Python full-stack engineer interview questions

If you’re interviewing for a more senior web-development position at a company that uses a Python web framework, you may encounter domain specific questions in Django or Flask. These questions will test your understanding of the frameworks and how you would use them in a practical context to build or expand on a web application. In addition to doing practice problems like the ones below, consider creating a small web application from the ground up to solidify your foundations in your chosen framework. If the position you’re interviewing for is full-stack, be sure to brush up on your front skills, like HTML and CSS, as well. 

Django interview questions

Question 1: Designing a basic Django model

Prompt: Design a Django model Book with fields for title (a string), author (a string), and publication_date (a date). Show how you would create a new instance of this model in the Django shell.

What this question evaluates: This question assesses understanding of Django models, one of the core components of Django. It tests knowledge of defining models and basic operations like creating new instances.

Solution:

from django.db import models
class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.CharField(max_length=100)
    publication_date = models.DateField()
# Creating an instance in the Django shell
# from myapp.models import Book
# book = Book(title='Sample Book', author='Author Name', publication_date='2023-01-01')
# book.save()

Explanation of solution: The Book model is defined with three fields: title, author, and publication_date. The model is a subclass of django.db.models.Model. An example is provided for creating an instance of Book in the Django shell, including saving it to the database.

Question 2: Creating Django views and URL configuration

Prompt: Write a Django view function to display a list of Book instances (from Question 1). Then, demonstrate how to configure the URL pattern for this view in Django’s URL dispatcher.

What this question evaluates: This question expands upon basic Django knowledge to include views and URL configurations. This assesses the ability to connect models to views and configure URL patterns, essential for building Django web applications.

Solution:

# views.py
from django.http import HttpResponse
from .models import Book
def book_list(request):
    books = Book.objects.all()
    output = ', '.join([book.title for book in books])
    return HttpResponse(output)
# urls.py
from django.urls import path
from . import views
urlpatterns = [
    path('books/', views.book_list, name='book_list'),
]

Explanation of solution: A view function book_list is created in views.py. It retrieves all Book instances and returns a simple HTTP response with the titles. The URL pattern for this view is defined in urls.py, mapping the route ‘books/’ to the book_list view.

Question 3: Implementing Django REST framework serializer

Prompt: Using Django REST Framework, create a serializer for the Book model. Then, write a view to handle a GET request that returns a JSON response containing all books using this serializer.

What this question evaluates: This question tests more advanced Django skills, focusing on Django REST Framework, a key tool for building APIs. It evaluates the understanding of serializers and viewsets for handling HTTP requests and generating JSON responses.

Solution:

# serializers.py
from rest_framework import serializers
from .models import Book
class BookSerializer(serializers.ModelSerializer):
    class Meta:
        model = Book
        fields = ['title', 'author', 'publication_date']
# views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from .models import Book
from .serializers import BookSerializer
class BookList(APIView):
    def get(self, request, format=None):
        books = Book.objects.all()
        serializer = BookSerializer(books, many=True)
        return Response(serializer.data)
# urls.py
from django.urls import path
from .views import BookList
urlpatterns = [
    # ... other url patterns ...
    path('api/books/', BookList.as_view(), name='api_book_list'),
]

Explanation of solution: A BookSerializer class is defined using Django REST Framework’s serializers.ModelSerializer. It specifies the model to serialize and the fields to include. A class-based view BookList is created, using APIView from Django REST Framework. It handles GET requests and returns a JSON response containing serialized data of all books. The corresponding URL pattern is added to urls.py, pointing to the BookList view for the route ‘api/books/’.

Flask interview questions

Question 1: Setting up a basic Flask application

Prompt: Describe how to set up a basic Flask application with a single route ‘/’ that returns the text “Welcome to Flask!” when accessed.

What this question evaluates: This question assesses fundamental knowledge of the Flask framework, focusing on application setup, route creation, and view functions.These skills are  essential for understanding the basic structure of a Flask application.

Solution:

from flask import Flask
app = Flask(__name__)
@app.route('/')
def home():
    return "Welcome to Flask!"
if __name__ == '__main__':
    app.run(debug=True)

Explanation of solution: A Flask app instance is created. A route ‘/’ is defined using the @app.route decorator. The corresponding view function home returns a simple string. The app.run(debug=True) statement runs the Flask application with debug mode enabled.

Question 2: Using Flask with template rendering

Prompt: Extend the basic Flask application from Question 1 to render an HTML template when accessing the ‘/’ route. Assume the HTML file is named index.html and located in a templates folder. The template should display “Welcome to Flask with Templates!”.

What this question evaluates: This question builds on the basic Flask setup to include template rendering, a key feature in Flask for displaying HTML content. It evaluates the candidate’s understanding of integrating Flask with HTML templates.

Solution:

# Assuming index.html is in the 'templates' folder
from flask import Flask, render_template
app = Flask(__name__)
@app.route('/')
def home():
    return render_template('index.html')
# index.html content:
# <html>
# <head><title>Flask Template</title></head>
# <body><h1>Welcome to Flask with Templates!</h1></body>
# </html>

Explanation of solution: The render_template function from Flask is used to render an HTML template. The home view function now returns render_template(‘index.html’), rendering the index.html file from the templates directory. The index.html file contains basic HTML to display a welcome message.

Question 3: Creating a Flask REST API endpoint

Prompt: Create a REST API endpoint in the Flask application that responds to GET requests at /api/data. It should return a JSON object with a key message and value “Flask API response”.

What this question evaluates: This question assesses the ability to build RESTful APIs with Flask, a common requirement in full-stack development. It tests for understanding of HTTP methods, route creation for APIs, and JSON data handling in Flask.

Solution:

from flask import Flask, jsonify
app = Flask(__name__)
@app.route('/api/data', methods=['GET'])
def get_data():
    return jsonify({'message': 'Flask API response'})
if __name__ == '__main__':
    app.run(debug=True)

Explanation of solution: The Flask route /api/data is defined to handle GET requests. The view function get_data returns a JSON response using jsonify, a Flask utility to convert Python dictionaries to JSON. The response contains a message. This setup demonstrates how to create a simple RESTful endpoint in Flask.

Python data science interview questions

Python is commonly used in data science for its simplicity and the large number of helpful libraries available, like NumPy and Pandas. Since data scientists come from a variety of backgrounds, including software development or more pure statistics, the level of coding ability expected in an interview will differ. Be upfront with interviewers about your background and experience; in general, however, familiarity with Python fundamentals and practical problems with your chosen libraries will help. If you’re newer to Python data manipulation, consider first solidifying fundamentals through online courses and personal projects before attempting interviews. Nothing beats experience in this domain.

NumPy interview questions

Question 1: Creating and manipulating NumPy arrays

Prompt: Write a NumPy script to create a 2×3 array of ones and then reshape it to a 3×2 array. Discuss the implications of reshaping an array in terms of data layout in memory.

What this question evaluates: This question tests basic understanding of NumPy array creation and manipulation, including array reshaping. It also assesses the candidate’s knowledge of NumPy’s memory management when modifying array shapes.

Solution:

import numpy as np
# Creating a 2x3 array of ones
array = np.ones((2, 3))
# Reshaping to a 3x2 array
reshaped_array = array.reshape((3, 2))
# Reshaping an array does not modify the underlying data in memory.
# It creates a new view on the existing data, arranged in the new shape.

Explanation of solution: A 2×3 array of ones is created using np.ones((2, 3)). The array is reshaped to 3×2 using reshape((3, 2)). Reshaping provides a new view on the same data, so it’s memory efficient as the data is not duplicated.

Question 2: Indexing and slicing an array

Prompt: Given a 2D NumPy array, write a function to select and return a subarray consisting of the first two rows and the last two columns. Explain how slicing affects memory usage and the relationship between the original and sliced arrays.

What this question evaluates: This question evaluates the candidate’s skills in array indexing and slicing, crucial for data manipulation in NumPy. It also tests for understanding of how slicing works in terms of memory (i.e., views vs. copies).

Solution:

import numpy as np
def select_subarray(array):
    # Selecting the first two rows and the last two columns
    subarray = array[:2, -2:]
    return subarray
# Slicing creates a view on the original array, not a copy.
# Changes to the subarray will affect the original array and vice versa.

Explanation of solution: The function select_subarray demonstrates slicing to extract a specific part of an array.  Rather than making copies, slicing creates a view, meaning the subarray shares data with the original array. This is memory efficient but requires care; because they reference the same data, modifying one affects the other.

Question 3: Using vectorized operations and broadcasting

Prompt: Describe how to perform element-wise multiplication of two 1D arrays of different lengths using NumPy’s broadcasting rules. Provide an example and explain the concept of broadcasting in NumPy.

What this question evaluates: This question assesses advanced understanding of NumPy, focusing on vectorized operations and broadcasting, which are key for efficient data manipulation. It tests the candidate’s ability to apply these concepts to solve problems with arrays of different shapes.

Solution:

import numpy as np
def multiply_arrays(a, b):
    # Element-wise multiplication using broadcasting
    return a * b
# Example: Multiplying arrays of different lengths
array1 = np.array([1, 2, 3])
array2 = np.array([4])
# Broadcasting rules allow this operation by 'stretching' array2
# to [4, 4, 3] before performing element-wise multiplication.
result = multiply_arrays(array1, array2)
# Broadcasting is a powerful concept in NumPy that allows vectorized operations
# on arrays of different sizes, making code efficient and concise.

Explanation of solution: The function multiply_arrays performs element-wise multiplication, showcasing NumPy’s broadcasting capabilities. Broadcasting automatically ‘expands’ smaller arrays for vectorized operations, avoiding explicit data replication and thus enhancing performance. The example illustrates how an array of length 1 (array2) is ‘stretched’ to match the size of array1 for multiplication, demonstrating the utility and power of broadcasting in NumPy.

Pandas interview questions

Question 1: Manipulating a dataframe

Prompt: Given a Pandas DataFrame, write a function to filter out rows where a specified column’s value is less than a given threshold and return the filtered DataFrame. For example, given a DataFrame with a column ‘Age’, filter out all rows where ‘Age’ is less than 30.

What this question evaluates: This question tests basic DataFrame manipulation skills, specifically filtering rows based on column values. It evaluates the candidate’s understanding of conditional selection in Pandas.

Solution:

import pandas as pd
def filter_dataframe(df, column, threshold):
    return df[df[column] >= threshold]
# Example usage
# df = pd.DataFrame({'Age': [25, 30, 45, 20]})
# filtered_df = filter_dataframe(df, 'Age', 30)

Explanation of solution: The function filter_dataframe filters rows in a DataFrame based on a column value threshold. It uses boolean indexing (df[column] >= threshold) to select rows where the column value meets the condition.

Question 2: Handling missing data

Prompt: How would you handle missing data in a Pandas DataFrame? Write a function that takes a DataFrame and fills missing values in a specified column with the column’s mean value.

What this question evaluates: This question assesses the candidate’s ability to handle missing data, a common issue in real-world datasets. It tests knowledge of Pandas methods for dealing with null or NaN values, specifically imputing missing data.

Solution:

import pandas as pd
def fill_missing_values(df, column):
    mean_val = df[column].mean()
    df[column].fillna(mean_val, inplace=True)
    return df
# Example usage
# df = pd.DataFrame({'Values': [1, 2, None, 4]})
# df_filled = fill_missing_values(df, 'Values')

Explanation of solution: The function fill_missing_values calculates the mean of the specified column and fills missing values with this mean. fillna is used with inplace=True to modify the original DataFrame. This is a common approach to handle NaN values in datasets.

Question 3: Using merge and join operations

Prompt: Explain how to perform merge and join operations between two DataFrames in Pandas. Provide an example function that takes two DataFrames and a key column, and returns a merged DataFrame based on that key.

What this question evaluates: This question focuses on understanding more advanced DataFrame operations, such as merging and joining, which are core techniques for combining datasets in data analysis tasks. It tests the candidate’s proficiency in manipulating and consolidating data from multiple sources.

Solution:

import pandas as pd
def merge_dataframes(df1, df2, key_column):
    return pd.merge(df1, df2, on=key_column)
# Example usage
# df1 = pd.DataFrame({'Key': ['A', 'B', 'C'], 'Data1': [1, 2, 3]})
# df2 = pd.DataFrame({'Key': ['B', 'C', 'D'], 'Data2': [4, 5, 6]})
# merged_df = merge_dataframes(df1, df2, 'Key')

Explanation of solution: The function merge_dataframes demonstrates how to merge two DataFrames using a common key column.pd.merge is a versatile function in Pandas for database-style joining of DataFrames. The example shows an inner join, but other join types (left, right, outer) can be specified with the how parameter.

Python machine learning & AI interview questions

As with data science, Python has emerged as the primary and most popular programming language for machine learning today. Interviews for such positions will likely cover some topics in Python. Although some positions may be open to candidates who demonstrate a strong foundational understanding and a willingness to learn, many machine learning and AI roles may require more advanced expertise. Interview questions evaluating familiarity and comfortability with Tensorflow tend to be more involved and can require data manipulation, as we demonstrate with the following practice problems. In addition to solving these questions, prepare by training additional models and staying informed about current machine learning trends.

TensorFlow interview questions

Question 1: Using basic TensorFlow operations

Prompt: Demonstrate how to create a TensorFlow constant tensor and a variable tensor. Perform a basic arithmetic operation (like addition) between them and print the result.

What this question evaluates: This question tests fundamental TensorFlow concepts, such as creating constants and variables, and performing tensor operations. It evaluates the candidate’s understanding of the basic building blocks of TensorFlow.

Solution:

import tensorflow as tf
# Creating a constant and a variable tensor
const_tensor = tf.constant([1, 2, 3])
var_tensor = tf.Variable([4, 5, 6])
# Performing addition
result = const_tensor + var_tensor
print(result.numpy())

Explanation of solution: A TensorFlow constant (tf.constant) and variable (tf.Variable) are created. These tensors are then added together using the + operator. The result is printed using the .numpy() method to convert the tensor to a NumPy array for easy visualization.

Question 2: Building and training a simple neural network

Prompt: Using TensorFlow, create a simple neural network model to classify handwritten digits (you can use the MNIST dataset). Describe the model architecture, compile the model, and outline the training process.

What this question evaluates: This question assesses the candidate’s ability to build and train a basic neural network using TensorFlow. It tests knowledge of model architecture, compiling models, and understanding training workflows.

Solution:

import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import SGD
# Load the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the images
train_images = train_images / 255.0
test_images = test_images / 255.0
# Building the model
model = Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])
# Compiling the model
model.compile(optimizer=SGD(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Training the model
model.fit(train_images, train_labels, epochs=5)
# Evaluate the model
model.evaluate(test_images, test_labels)

Explanation of solution: The solution involves loading the MNIST dataset and normalizing the image data. A sequential model is built using Dense layers, including a flatten layer for the input and a softmax activation for the output. The model is compiled with the SGD optimizer and sparse categorical cross-entropy loss function. The model is trained using the fit method and evaluated on test data.

Question 3: Implementing custom loss functions

Prompt: Write a custom loss function in TensorFlow and demonstrate how to use it in training a model. Explain in what scenarios custom loss functions are necessary and how they are integrated into the training process.

What this question evaluates: This advanced question explores the candidate’s ability to customize aspects of the neural network training process. It assesses understanding of loss functions in TensorFlow and how to implement and integrate custom functionalities.

Solution:

import tensorflow as tf
# Custom loss function
def custom_loss_function(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_true - y_pred))
# Example model (could be any model)
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(input_shape,)),
    tf.keras.layers.Dense(1)
])
# Compile the model with the custom loss function
model.compile(optimizer='adam', loss=custom_loss_function)
# Train the model
# model.fit(X_train, y_train, epochs=10)

Explanation of solution: A custom loss function is defined, which calculates the mean squared error between the true and predicted values. An example neural network model is defined using the Sequential API. The model is compiled, specifying the custom loss function in the loss parameter. This approach allows for flexibility in model training, particularly in scenarios where standard loss functions are inadequate or need customization.

Next steps & resources

As you continue preparing for your Python interview, it’s important to delve deeper into the concepts and problems we’ve discussed. Practice is key—try to implement these problems and their solutions on your own, experiment with variations, and explore additional challenges in the topics most relevant for your job search. 

Beyond coding, familiarize yourself with Python’s ecosystem, including popular libraries and frameworks relevant to your field, whether it’s web development, machine learning, or another specialization. Engaging with community resources—online forums, coding challenges, and open-source projects—can provide practical experience and expose you to real-world applications of Python that can expand your knowledge. Practicing interview scenarios, either solo or with a peer, can also help you build confidence and improve your problem-solving speed.

CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

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Let’s face it: Technical interviews can be burdensome and unpleasant—for interviewers and candidates alike. For hiring teams, designing interview and assessment questions that accurately assess a candidate’s technical aptitude, problem-solving skills, and ability to collaborate can be a challenge. Candidates, meanwhile, face the pressure of demonstrating their technical knowledge and skills while communicating effectively in what is often a stressful interview context. 

Here at CodeSignal, the leading technical interview and assessment platform, we have spent countless hours developing solutions to make technical interviews less painful for both interviewers and candidates. Hiring teams who use CodeSignal benefit from a cohesive platform that supports pre-hire screening assessments and live technical interviews built around the same advanced integrated development environment (IDE), which creates a seamless experience for candidates as they progress through your hiring funnel. 

Working with hundreds of top companies, we’ve learned what makes a great technical interview—and what interviewers and candidates need to know about the tech hiring process. We’ve distilled this knowledge into this comprehensive resource designed to help both interviewers and candidates navigate the technical interview process. This guide equips interviewers with effective techniques for assessing candidates’ technical abilities, while also providing candidates with valuable tips for acing their interviews. 

Skip ahead to a section of the guide here:

• What is a technical interview?
• What are the different stages of technical interviews?
• What are the different formats of technical interviews?
• Technical interview perspectives: engineering candidate vs. interviewer
• Technical interview tips for recruiters & engineering managers
• Technical interview tips for software engineer candidates
• Technical interview do’s and don’ts
• Common technical interview scenarios and how to handle them
• Final thoughts

What is a technical interview?

A technical interview is a method employed by recruiters to evaluate candidates for software engineering and other technical roles. Technical interviews often focus on assessing a candidate’s job-relevant technical skills, problem-solving abilities, and communication style. The insights gathered from these interviews should help employers decide how successful a candidate would be in the role they’re applying for.

What are the different stages of technical interviews?

The different stages hiring for technical roles may vary depending on the company and engineering role, but usually, technical interviews can be broken down into the following stages:

Initial screening and assessment

Companies often begin the hiring process for technical roles with pre-screen assessments to hone in on qualified candidates. By incorporating these preliminary evaluations, employers can efficiently narrow down the applicant pool to a more manageable number of well-suited candidates early in the hiring process. Pre-screen assessments not only save time and resources for the hiring team, but also ensure that the candidates advancing to the next stages possess the necessary expertise and aptitude for the role. They are generally preferred to resume review as a method for initial screening candidates for technical roles.

CodeSignal Pre-Screen is a powerful tool for assessing engineering candidates at the top of the recruiting funnel. With Pre-Screen, companies can effectively streamline the initial evaluation process with objective and validated skills assessments. Since Pre-Screen Certified Evaluations are developed, validated, and maintained by CodeSignal, engineering teams reduce the amount of time they spend on recruiting by thousands of hours per year. Furthermore, Pre-Screen helps minimize bias in the hiring process, as it emphasizes objective metrics and skill-based evaluations rather than relying on factors such as educational background or previous work experience. 

Behavioral interview

The behavioral interview stage of the engineering hiring process focuses on evaluating a candidate’s soft skills, interpersonal abilities, and overall fit within the company culture. Unlike the technical interview, which assesses the candidate’s coding skills and other problem-solving abilities, the behavioral interview delves into their past experiences, work habits, and personality traits. Interviewers often pose situational or hypothetical questions to gauge the candidate’s ability to handle challenges, work effectively within a team, and adapt to an organization’s values and expectations. 

Technical interview

During the technical interview stage of the engineering hiring process, candidates are generally asked to showcase their coding abilities in real-time using an integrated development environment (IDE). This stage is a crucial opportunity for both the candidate and the engineering hiring manager to gain a deeper understanding of each other’s working style and thought processes. In this scenario, the candidate typically works on a coding problem or a small project while the hiring manager observes and assesses their problem-solving skills, technical knowledge, and overall approach. The collaborative nature of the exercise allows the hiring manager to ask clarifying questions, gauge the candidate’s communication skills, and evaluate their ability to adapt under pressure. Solutions like CodeSignal Pre-Screen provide a research-backed technical screening solution to significantly reduce the burden of technical interviewing on engineering teams.

On-site interview

The on-site interview stage is the final step in the engineering hiring process, designed to provide a comprehensive evaluation of the candidate’s technical and communication skills. Conducted by a panel of engineers, this stage typically involves a live coding exercise in which the candidate is tasked with solving complex problems or working on a project relevant to the role. The panel assesses the candidate’s problem-solving approach, coding proficiency, and ability to think on their feet. 

Additionally, the on-site interview allows the panel to observe the candidate’s communication and collaboration skills, as they may be required to discuss their thought process, ask questions, or seek feedback from the panel members. This immersive, interactive experience offers valuable insights into the candidate’s potential fit within the team and their capacity to excel in the engineering role. CodeSignal Interview is a live interviewing tool designed for on-site and panel interviews and provides a realistic development environment with built-in video calling for interviewers to evaluate candidates’ job-relevant skills.

Post-interview debrief

The final interview debrief is a crucial step in the engineering hiring process, as it allows the interviewers to come together and thoroughly discuss the candidate’s performance throughout the various stages of the evaluation. In order to objectively assess the candidate’s job-relevant skills, interviewers often employ a numerical rubric, assigning scores based on predefined criteria, such as technical expertise, problem-solving abilities, and collaboration skills By utilizing this consistent method of evaluation, the hiring team can compare candidates more accurately, ensuring that the final decision is rooted in objective data rather than subjective opinions. 

What are the different formats of technical interviews?

Online coding assessment

An online coding assessment is a standardized evaluation tool used by employers to gauge a candidate’s programming skills and other problem-solving abilities during the technical hiring process. These assessments typically consist of coding challenges or exercises that require candidates to write, analyze, and debug code. Online coding assessments also streamline the hiring process, allowing companies to efficiently filter potential candidates and identify top talent for their technical teams.

Multiple choice quiz

A multiple-choice quiz for technical hiring is an evaluation method used to assess a candidate’s knowledge of specific programming languages, frameworks, or concepts. These quizzes typically present a series of questions or problem statements, requiring the candidate to select the correct answer from a list of options. Although multiple-choice quizzes can provide a quick and straightforward way to gauge a candidate’s theoretical understanding, they have limitations in accurately measuring a candidate’s practical coding skills or ability to solve complex, real-world problems. As a result, these quizzes may not provide a comprehensive view of a candidate’s capabilities, potentially leading to poorly-informed hiring decisions.

Take home assignment

A take-home assignment for technical hiring is a project-based assessment that requires candidates to complete a coding or engineering task outside of the interview setting, often simulating real-world scenarios or problems. These assignments provide valuable insights into a candidate’s problem-solving abilities, creativity, and practical skills. However, take-home assignments can be time-consuming for candidates, as they may require several hours or even days to complete. This extended time commitment can be a deterrent for highly skilled candidates with limited availability, potentially causing them to opt out of the application process or seek opportunities with a less burdensome hiring process.

Whiteboard interview

A whiteboard interview for technical hiring is a traditional evaluation method in which candidates are asked to solve coding problems or design algorithms on a whiteboard, often in front of an interviewer or panel. This approach is intended to assess a candidate’s thought process, problem-solving abilities, and communication skills. However, whiteboard interviews have notable limitations, as they can create an artificial, high-pressure environment that may not accurately reflect a candidate’s true capabilities or the day-to-day work experience (since real code isn’t written on a whiteboard). Additionally, these interviews may inadvertently favor candidates with strong presentation skills over those with stronger technical abilities but who may not perform well under such stressful conditions.

Technical interview perspectives: engineering candidate vs. interviewer

What’s the candidate’s perspective on technical interviews? 

From the software engineering candidate’s perspective, technical interviews can be both exciting and nerve-wracking. On the one hand, technical interviews provide an opportunity for candidates to showcase their coding skills, problem-solving abilities, and experience. On the other hand, the pressure to perform well and the fear of being asked a question they don’t know the answer to can be intimidating.

What’s the interviewer’s perspective on technical interviews? 

While technical interviews can be challenging for candidates, they can also be challenging for interviewers. Interviewers must be able to assess a candidate’s abilities objectively and fairly, while also providing a positive and welcoming experience for the candidate. They may need to adjust their approach based on the candidate’s experience level, communication style, or learning style.

Technical interview tips for recruiters & engineering managers

How should you conduct a technical interview?

To conduct a technical interview effectively, start by defining the specific skills and knowledge required for the position, and develop a clear evaluation criterion to assess the candidate’s abilities. Ideally, this step would involve engaging IO Psychologists in a job analysis that identifies the knowledge, skills, abilities, and other characteristics (KSAOs) required for a candidate to succeed in the role. 

A technical interview may incorporate a combination of evaluation methods, such as coding tasks and knowledge-based questions, to obtain a comprehensive understanding of the candidate’s technical capabilities, problem-solving skills, and ability to communicate complex concepts. Ideally, the interview process should be structured to minimize stress and provide a realistic representation of the candidate’s day-to-day work environment.

CodeSignal provides pre-screen assessment, full-service technical interview, and live coding interview solutions that provide an advanced, realistic integrated development environment (IDE) for candidates and interviewers. With CodeSignal Interview, for instance, interviewers can observe a candidate’s coding process in real-time, engage in collaborative problem-solving, and even pull up a candidate’s pre-screen assessment results in the coding environment. This environment provides a “flight simulator” experience for candidates while enabling interviewers to assess the candidate’s skills effectively and efficiently. 

What interviewers should look for when hiring software engineers

When hiring software engineers, interviewers should look for a combination of technical skills, problem-solving abilities, and communication skills. A strong foundation in programming languages, frameworks, and algorithms is important, and the capacity to learn and adapt to new technologies is essential. Equally important is the candidate’s ability to analyze complex problems, develop creative solutions, and collaborate effectively within a team. Finally, soft skills such as communication, time management, and resilience are crucial, as they enable the candidate to navigate challenges, articulate ideas, and contribute positively to a team’s overall success and growth.

Technical interview tips for software engineer candidates

Approaching a technical interview as a candidate can feel quite daunting, particularly if it’s been some time since you’ve taken a similar assessment. Your performance in these interviews greatly influences the hiring decision, so it’s important to prepare well. 

How to make a great impression in your technical interview as a software engineer

As a software engineer, making a great impression in your technical interview is essential for showcasing your skills and securing the job offer. To ensure success, consider the following strategies:

• Prepare thoroughly: Brush up on your knowledge of programming languages, data structures, and algorithms, and familiarize yourself with the technologies and frameworks relevant to the job.
• Practice problem-solving: Work through coding challenges or sample interview questions to hone your problem-solving skills and develop a structured approach to tackling complex tasks.
• Demonstrate clear communication: Articulate your thought process while solving problems, ask clarifying questions when needed, and explain your solutions in a concise, understandable manner.
• Showcase teamwork and adaptability: Be receptive to feedback, collaborate effectively with interviewers, and demonstrate a willingness to adapt your approach when presented with new information.
• Show confidence and professionalism: Present yourself confidently, maintain a positive attitude, and exhibit enthusiasm for the role and the company.

How to prepare for a technical interview

1. Research the company and role

When applying for a software engineering role, it’s crucial for you to research the company and the specific position thoroughly. This research should involve understanding the company’s products, services, and overall mission. By gaining insight into the company culture, team dynamics, and tech stack, you can tailor your approach during the interview process, demonstrating a genuine interest in the organization and showcasing your ability to be a valuable contributor to the team’s success.

2. Review technical concepts and skills specific to the role

Before your first interview for a software engineering role, make sure to thoroughly review the technical concepts and skills specific to the position. This may involve refreshing your knowledge of programming languages, frameworks, or tech stack listed in the job description, and practicing relevant coding challenges or exercises.

3. Practice online coding challenges

Software engineering candidates seeking effective preparation for upcoming technical interviews should consider engaging in coding interview challenges. Regular practice with these challenges allows you to refine your problem-solving abilities, enhance your algorithmic thinking, and become acquainted with the structure of technical interview questions. Incorporating practice coding challenges into your interview preparation will boost your confidence—and performance—in technical interviews.

4. Prepare questions to ask the interviewer

Preparing questions to ask your interviewer when interviewing for a software engineering position demonstrates your interest in the role and showcases your proactive attitude. Ask insightful questions to help you better understand the company culture, team dynamics, and expectations of the role—and ensure that the position aligns with your career goals and values. For example, you might consider asking about the company’s approach to software development methodologies, or how the team handles code reviews and maintains code quality. 

What to expect during your technical Interview

As a software engineering candidate, you should be prepared for a range of topics and coding challenges during your technical interview. These interviews typically assess your coding skills, problem-solving abilities, and understanding of computer science fundamentals: algorithms, frameworks, data structures, and more. 

You may be asked to write code on a whiteboard or in an online coding environment, solve algorithmic puzzles, or discuss architectural design principles. The interviewer might also delve into your past experiences, asking you to explain your thought process or approach to specific projects. Remember: the primary goal of the technical interview is to evaluate not just your technical proficiency, but also your ability to communicate and collaborate effectively with others. Keep a positive attitude, demonstrate your thought process, and don’t be afraid to ask clarifying questions if needed. 

Examples of technical interview questions & answers

It’s impossible to provide an exhaustive list of interview questions you might encounter as part of this guide. The type of questions will vary depending on the role, technologies, and the type of position that you are interviewing for. Nevertheless, here are a few examples of the types of questions you might encounter.

Question: How would you reverse a string in your preferred programming language?

Acceptable answer: In Python, you can reverse a string using slicing notation with the following code:

reversed_string = original_string[::-1]

Alternatively, you can use the built-in reversed() function and join the characters:

reversed_string = ''.join(reversed(original_string))

Question: What is the difference between a stack and a queue data structure?

Acceptable answer: A stack is a last-in, first-out (LIFO) data structure, meaning the last element added is the first one to be removed. It supports two main operations: push (add an element) and pop (remove the most recently added element). A common example of a stack is a pile of plates; you add and remove plates from the top.

A queue, on the other hand, is a first-in, first-out (FIFO) data structure, meaning the first element added is the first one to be removed. It supports two main operations: enqueue (add an element to the end) and dequeue (remove the element from the front). A real-life example of a queue is a line of people waiting in a queue; people join at the end and leave from the front.

Technical interview do’s and don’ts

Technical Interview “Do’s”:

• Review computer science fundamentals, such as data structures, algorithms, and frameworks.
• Practice solving coding problems on various online platforms and in different programming languages.
• Clearly communicate your thought process and problem-solving approach throughout the interview.
• Ask clarifying questions if you’re unsure about the task or requirements.
• Manage your time effectively, focusing on the most important aspects of the task.
• Be prepared to discuss your past experiences and projects, emphasizing your role and contributions.
• Demonstrate your ability to work collaboratively by being receptive to feedback and suggestions.
• Research the company and its culture to show genuine interest and ask insightful questions.

Technical Interview “Don’ts”:

• Avoid memorizing specific solutions or code snippets, as interviewers may use different questions in your interview.
• Don’t underestimate the importance of soft skills, such as communication and teamwork.
• Don’t be afraid to admit when you don’t know something. Honesty is better than bluffing.
• Refrain from being defensive or argumentative if the interviewer challenges your solution.
• Don’t rush through the problem. Instead, take your time to understand the requirements and constraints.
• Avoid getting stuck on minor details or syntax. Focus on the core logic and problem-solving.
• Don’t let nerves or anxiety overshadow your abilities. Stay calm and composed throughout the interview.
• Refrain from giving generic answers to questions about the company or your motivation for applying.

Common technical interview scenarios and how to handle them

For candidates

Scenario 1: The interviewer asks a technical question you don’t know the answer to

You may encounter technical questions during an interview that you don’t know the answer to—it happens. In these situations, it’s crucial to remain calm and composed. Rather than trying to bluff your way through, be honest about your lack of knowledge, and express your willingness to learn. You can demonstrate your problem-solving abilities by attempting to reason through the question, discussing your thought process, and acknowledging any gaps in your understanding. If possible, relate the unknown topic to something you are familiar with, showing that you can adapt and leverage your existing knowledge. Interviewers appreciate candidates who can handle uncertainty gracefully and exhibit curiosity, humility, and a growth mindset in the face of challenges.

Scenario 2: You finish the coding challenge early and have extra time

If you complete a coding challenge during a technical interview ahead of schedule, it’s important to use the extra time wisely. First, double-check your solution for correctness, edge cases, and potential optimizations. This demonstrates your attention to detail and thoroughness. Next, consider the readability and maintainability of your code. Ensure that your code is well-structured, uses meaningful variable names, and includes comments where necessary to clarify complex logic. If appropriate, discuss alternative approaches or trade-offs with the interviewer, showcasing your depth of understanding and ability to evaluate different solutions. Finally, be prepared to answer any follow-up questions from the interviewer, as they may want to probe your knowledge or challenge your assumptions. Using your extra time effectively demonstrates your commitment to excellence and sets you apart as a strong candidate.

For interviewers

Scenario 1: Candidate struggles with a coding challenge

As an interviewer, when you notice a candidate struggling with a coding challenge, you may choose to provide guidance and support without giving away the solution. Start by encouraging the candidate to verbalize their thought process and ask clarifying questions, as this can help identify misunderstandings or gaps in their approach. Offer gentle hints or suggest breaking the problem into smaller, more manageable parts. Remember to maintain a positive and empathetic demeanor, as it can help alleviate anxiety and create a more conducive environment for the candidate to showcase their problem-solving abilities. If the candidate continues to struggle, consider modifying the question’s difficulty or shifting the focus to another aspect of the challenge that aligns better with their skillset. Regardless of your approach, be sure to offer consistent support for each candidate to prevent unfairness or bias in your hiring process.

Scenario 2: Candidate is overconfident

If a candidate seems overconfident or dismissive of feedback, try gently challenging their assumptions and providing constructive criticism. Ask follow-up questions to clarify their thought process and provide feedback on areas where they could improve. It’s important to balance this with positive feedback and encouragement so the candidate doesn’t become discouraged.

Final thoughts

This guide to technical interviews has provided comprehensive documentation of what technical interviews are, as well as different types and formats of technical interviews. It has also offered guidance to interviewers on how best to assess candidates’ job-relevant skills, and advice for candidates on how to prepare for a technical interview, what to expect during the interview, and how best to handle challenges that arise. For technical interviews that help companies identify and secure a “yes” from their top candidates, hundreds of companies turn to CodeSignal.

Want to learn more about how CodeSignal can elevate your technical interviews and help you make the right hires, faster? Schedule a call with one of our tech hiring experts.

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Question 1: Array Manipulation

Taking a look at this question, you can see that it covers a basic array traversal and manipulation. The candidate simply needs to return the sum of each value in a, plus its right and left neighbors. 

At the same time, the question asks candidates to take into account corner cases with their implementation, which is an important fundamental skill. They need to correctly handle the first and last elements of the array. 

Question 1: Solution

Above is a simple 10-line implementation in Python. First, the solution gets the length of a and then creates a zeroed-out array b of the same length. Next, it uses a for loop to traverse through b and complete the operations as specified. Note that this solution handles the edge cases by checking if the a index is in range before adding the value to b.

Question 2: String Pattern Matching

This is a pattern-matching question where instances of a pattern need to be found inside of a larger array. It has the advantage of testing several fundamental programming skills at once: traversing multiple arrays with nested loops, working with subarrays, and performing basic collections/string operations.

Note that the guaranteed constraints in this question indicate that the candidate shouldn’t worry about optimizing their solution. 

Question 2: Solution

In this solution, the candidate first defines the vowels. Then, they define a helper function, check_for_pattern, that their solution will call for every possible substring in source. For each character in the pattern string, the helper function checks if the character in the substring matches the pattern (0 for vowel, 1 for consonant). 

Question 3: Two-Dimensional Array Traversal 

You are given a matrix of integers field of size height × width representing a game field, and also a matrix of integers figure of size 3 × 3 representing a figure. Both matrices contain only 0s and 1s, where 1 means that the cell is occupied, and 0 means that the cell is free. You choose a position at the top of the game field where you put the figure and then drop it down. The figure falls down until it either reaches the ground (bottom of the field) or lands on an occupied cell, which blocks it from falling further. After the figure has stopped falling, some of the rows in the field may become fully occupied. Your task is to find the dropping position such that at least one full row is formed. As a dropping position, you should return the column index of the cell in the game field which matches the top left corner of the figure’s 3 × 3 matrix. If there are multiple dropping positions satisfying the condition, feel free to return any of them. If there are no such dropping positions, return -1. Note: The figure must be dropped so that its entire 3 × 3 matrix fits inside the field, even if part of the matrix is empty.  Example For field = [[0, 0, 0],          [0, 0, 0],          [0, 0, 0],          [1, 0, 0],          [1, 1, 0]] and figure = [[0, 0, 1],          [0, 1, 1],          [0, 0, 1]] the output should be solution(field, figure) = 0. Because the field is a 3 x 3 matrix, the figure can be dropped only from position 0. When the figure stops falling, two fully occupied rows are formed, so dropping position 0 satisfies the condition. For field =  [[0, 0, 0, 0, 0],           [0, 0, 0, 0, 0],           [0, 0, 0, 0, 0],           [1, 1, 0, 1, 0],           [1, 0, 1, 0, 1]] and figure = [[1, 1, 1],           [1, 0, 1],           [1, 0, 1]] the output should be solution(field, figure) = 2. The figure can be dropped from three positions: 0, 1, and 2. As you can see below, a fully occupied row will be formed only when dropping from position 2: For field =  [[0, 0, 0, 0],           [0, 0, 0, 0],           [0, 0, 0, 0],           [1, 0, 0, 1],           [1, 1, 0, 1]] and figure = [[1, 1, 0],           [1, 0, 0],           [1, 0, 0]] the output should be solution(field, figure) = -1. The figure can be dropped from two positions, 0 and 1, and in both cases, a fully occupied row won’t be obtained: Note that the figure could technically form a full row if it was dropped one position further to the right, but that is not a valid dropping position, because the 3 x 3 figure matrix wouldn’t be fully contained within the field.

This Tetris-inspired question is challenging. Candidates will have to traverse and manipulate two-dimensional arrays in a non-conventional way. As it’s an advanced question, it tests how well an engineer can understand a complex set of constraints and proceed with a non-trivial implementation.

Question 3: Solution

This solution starts by defining some dimensions that will be important for the problem. Then, for every valid dropping position—that is, columns in range(width - figure_size + 1)—the code goes row by row, seeing how far the figure will go until it will “stop.” 

To do this, it peeks at the next row and asks: can the figure fit there? If not, it stops at the previous row. The figure doesn’t fit if there is a 1 in the same place in both the figure and the field (offset by the row). The loop can stop when row == height - figure_size + 1, because then the bottom of the figure will have hit the bottom of the field.

Once the code figures out the last row the figure can drop to before it can’t fit anymore, it’s time to see if there’s a fully-filled row created anywhere. From where the figure “stopped,” the code iterates through the field and figure arrays row by row, checking to see if together, they’ve created a filled row. Cleverly, the code sets row_filled = True and then marks it False if one or both of these conditions are met:

• Any of the field cells in the row are empty (not 1)
• Any of the figure cells in the row are empty (not 1)

Question 4: Lookup Table 

This problem could be solved in a straightforward way by having two nested loops to choose each pair and check whether their sum is a power of two. But since the numbers array could be quite large, quadratic time complexity would be too much for this question. (To get more precise, it is O(n2 * log(MAX_NUMBER)) where MAX_NUMBER is the largest number seen in the array.)

Therefore, this question tests whether candidates have the problem-solving and data structures skills to use a lookup table (hash set/dictionary) in their programming language of choice. It also involves a bit of tricky logic to avoid double-counting pairs. Finally, this question asks candidates to pay close attention to constraints, testing a key skill for real-world development.

Question 4: Solution

This Python solution uses collections.defaultdict to create a dictionary of integers. For each element in the array, the element is first counted in the dictionary. Then, the loop checks for sums of all of the powers of two that are less than 221. Why stop there? The constraint says that no element of the array can be greater than 106, or 1,000,000. 221 is 2,097,152, which is greater than 1 million when divided by two, so there is no way that two elements could sum to 221. 

To calculate the powers of two, the code uses a left shift bitwise operator: 1 << two_power in Python is the same as 2two_power. It defines second_element as the amount needed for the current element to sum to the power of two. Then it looks up the count for second_element in the dictionary. The number of second_elements already found in the array gets added to the answer, because this is the number of pairs (so far) that sum to that power of two. By the time it reaches the end of the array, this algorithm will have found all the pairs—in linear time.

We hope this has given you some insight into the kinds of technical interview questions we ask at CodeSignal. For developers looking to practice or further build their skills, check out CodeSignal Learn.

Learn a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

Note: If you’re a hiring manager wanting to learn more about our skills evaluation frameworks and how we approach question design, schedule a discovery call with us today. 

Dmitry Filippov is the Technical Assessment Lead at CodeSignal, where he manages content development for all of CodeSignal’s technical assessments. He received his Master’s degree in Computer Science from ITMO University, and has been an active member of the International Collegiate Programming Contest community for over 10 years.

The post Example CodeSignal questions for software engineering interviews appeared first on CodeSignal.

At this point, you might be in the middle of searching for a new grad developer job. As you prepare, look out for myths about the hiring and interview process. We’re here to share the reality of what actually goes on in the world of tech hiring today, so you don’t waste any time landing the software engineering role of your dreams. 

Myth: Your resume is everything. 

Reality: More and more companies are moving to skills-based screening instead of resumes.

Does thinking about what’s on your resume—or more importantly, what’s not on it—give you a sense of mild panic? That’s normal. Society teaches us that resumes are all that matter in order to land your first interview. 

The good news is many companies, including some of the largest tech companies like Uber, Meta, Zoom, and Robinhood, have wisened up to the fact that the top software engineering talent doesn’t all come from the same “Big 5” engineering schools. They’re using skills-based assessments to complement, and in many cases replace, their resume screen for new grad developers. The idea is: why should your resume matter if you can write high-quality code?

So, instead of spending a lot of time crafting the perfect resume, it’s a good idea to practice solving coding challenges in a realistic development environment. It may take some effort, but at least it’s work that will actually pay off on the job.

Myth: Getting a “yes” is all about luck. 

Reality: Software engineering interviews should be structured and predictable. 

If you look online, there’s a lot of chatter about how interviewing for software engineering is basically like rolling the dice. You have to line up the perfect combination of questions you know the answers to and interviewers you connect with. Then, keep trying and trying to maximize your chances. 

It’s true: persistence is important. But from a hiring perspective, companies that care about hiring the best people don’t want you to have to be lucky. If they do, that means their processes for new grads aren’t very consistent, and they aren’t getting a strong signal from their interviews. Furthermore, needing to be lucky is also usually a sign that their hiring process is biased. 

If you’re interviewing for a new grad software engineering job, the process should be consistent and structured. You can and should ask what this process entails, who you’ll be speaking to, and what kinds of questions you should prepare for. You should ask about the coding interview platform that they’re using, and what you can do to practice beforehand. For example, you can practice questions on CodeSignal’s platform here.

Myth: If you aren’t super confident in your coding skills, you probably aren’t cut out for software engineering. 

Reality: If you have the fundamentals, the rest can be taught. 

As you prepare to interview for new grad software engineering jobs, you might be feeling some imposter syndrome. You could be worried that you have to be a “wizard” or “ninja” to get a job—when you’re more like a normal human who constantly looks things up on Stack Overflow. 

This fear tends to be amplified for candidates from underrepresented backgrounds who may not have seen many others in the field who look like them or share their experiences. They may have less exposure to software engineering as a career than their new grad peers. 

But as long as you have some computer science fundamentals, you can and will grow your skills on the job. One of the things that’s exciting about a career in software engineering is that the learning is nearly endless because the pace of change in the industry is so high. Hiring teams don’t care as much about what you know right now—they care about whether you will be curious, collaborative, and eager to learn.

Myth: You’re on your own and the competition is cutthroat. 

Reality: Other engineers out there care about helping you find a job. 

In school, it can sometimes feel like you’re competing against your classmates, trying to stay ahead of the curve. Then, you go straight to competing for a new grad software engineering job. You might feel like it’s you against the world, which is a lonely place to be.

In reality, software engineering is a very collaborative discipline. Just look at the open source community, or all the blogs and forums where engineers spend time sharing solutions to technical problems. Interviews are really no different: everyone you speak with is hoping that you’ll succeed. Logically, they want to find someone for the role and they don’t want to have to spend more time interviewing. Thinking about interviews this way—as an environment where everyone is rooting for you—can be a powerful mental shift. 

This also means that you shouldn’t be afraid to look for help, such as asking a fellow developer to give you a practice interview. There are also nonprofit organizations that you should investigate, such as ColorStack, which helps Black and Latinx grads get the support they need to have fulfilling careers in tech. 

We hope this article helped you feel more prepared to get your first new grad software engineering job. For more interview prep advice, check out our tips for virtual coding interviews and this coding assessment checklist.

Want to practice or build your skills? CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

The post Getting a new grad software engineering job: Myths vs. reality appeared first on CodeSignal.

1. Make sure you know what to expect.

You might not know what questions you’ll be asked, but the other details of the interview needn’t be a mystery. Reach out to your recruiter with any questions—they’re there to help you in the process and they should be able to give you all the information you need to confidently plan and prepare. 

It’s also useful to check with your recruiter about any audio/video technology that might be used during your interview. CodeSignal’s interview platform has a built-in videoconference tool so you won’t need to simultaneously navigate multiple windows. 

2. Know your languages. 

Many roles expect you to code in a specific language, so it’s a good idea to know in advance the ones you might be expected to use. Make sure to practice with your programming language of choice beforehand, so that during the technical interview you’ll be able to focus on the problem without being tripped up by syntax. Many coding assessment platforms, including CodeSignal, support a variety of languages—so you can work with the one you know best.

3. Practice in a realistic environment.

It’s a good idea to try out the IDE that you’ll be using in your technical interview ahead of time. This can help you get familiar with how the assessment platform works. CodeSignal’s IDE is built on the same framework as VSCode to make it familiar and comfortable for coding. You can take practice assessments before your interview, so you can get used to customizing the language, theme, and formatting in the way that works best for you. If you’ve prepared with CodeSignal before, you won’t even need to create a new account.

4. Review your previous work.

Go through the code for some of your past projects. Since you might be building out a feature in your technical interview, being able to quickly refamiliarize yourself with past projects hones an important skill. It also helps make sure that you have a few examples at the front of your mind to answer questions about past experience with debugging or implementing specific features. This can also serve as a helpful reminder that you’ve done this sort of work before!

5. Rehearse what you’ll say.

During your technical interview, it’s really helpful to narrate what you’re doing so your interviewer has a sense of your thought process. This can feel unnatural if you’re not used to it, so practice talking as you code even when you’re doing sample problems. This way, talking as you code becomes a tool to help articulate your thinking, rather than a distraction from other tasks in the moment. 

It’s also useful to practice your introduction and to prepare some examples that demonstrate your skills so you have familiar phrasing to fall back on during the interview.

6. Set yourself up in a good head space.

Make sure the rest of your life is settled away—set out an outfit, pull up the webinar link, and plan a nutritious breakfast or lunch so you’re not worrying about those details on the day of your technical interview. Also, do your best to get a full night’s rest the night before, and remember that it’s normal and even expected to be nervous!

7. Tidy up and dress up.

Dress for your virtual technical interview the same way you’d dress for an in-person interview: business casual is great. Take care to clean up your background beforehand, and do your best to make it a quiet, well-lit, and distraction-free space so you’ll be easy to see and hear.

8. Check your technology setup. 

Do a thorough check of all the technology that you’ll be relying on during your technical interview, so you aren’t scrambling the day of. Make sure you’re using a browser that will support all features that your technical interview will use, since some platforms require certain browsers to allow video conferencing. CodeSignal recommends an up-to-date version of Mozilla Firefox or Google Chrome for the best experience. 

Check your internet connection, test links, and make sure your laptop is charged or plugged in. It can also be helpful to tidy up your tabs in the window you’ll be working in and to turn off desktop notifications to avoid distraction during the technical interview.

Best of luck! You’ve done a great job setting yourself up for success. Even if this technical interview doesn’t go as you hope, know that the work you’ve done to prepare will still serve you well in the long run since it means you’ll have had this experience before. 

Want to practice or level up your skills before your next interview? CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

The post 8 tips to make sure you’re ready for your virtual technical interview appeared first on CodeSignal.

• Have you brushed up on your coding skills? CodeSignal Learn offers short, engaging learning paths designed to help you prepare for technical interviews and assessments in today’s most common coding languages. If you’re preparing to take CodeSignal’s General Coding Framework, for instance, the Four-Week Coding Interview Prep in Python learning path will help you hone the fundamental coding skills you’ll be assessed on.

• Have you taken a practice assessment? This will help you get acquainted with the CodeSignal IDE and know what to expect. Here’s a link to a CodeSignal practice assessment. You can check out our guide to preparing for a technical interview.

• Do you know what language you’ll be coding in? Are you a Java, Python, or C++ person? Since many platforms allow you to code in the language of your choice, you should pick the one you’re most comfortable with, keeping in mind that you might be able to write code faster in a less verbose language. And don’t forget to brush up on the syntax and standard library functions so you don’t have to spend time looking things up on the fly. 

• Do you have quick access to documentation? Speaking of looking things up, did you know that most platforms (including CodeSignal) allow you to access the internet during the assessment? If you’re taking a Pre-Screen assessment on CodeSignal, you’re permitted to search for syntax-related questions online.

• Do you have a time management strategy? As most assessments are timed, you may want to move on if you find yourself spending too long on one part. CodeSignal’s General Coding Framework, for example, has four tasks to complete within 70 minutes. Consider, as well, that the tasks are likely to be varying levels of difficulty. Therefore, it may make sense to come back to a tough question later after solving the others first.

• Have you eliminated all distractions? You want to be in the zone during your assessment and experiencing that coding flow state. It’s a good idea to close any extraneous apps and windows, silence your notifications, and let housemates know not to disturb you. Also, this might seem obvious, but low battery life has a way of sneaking up on us at the worst times — so make sure your computer is plugged in! 

• Did you get a good night’s sleep? Unsurprisingly, studies have shown that getting more than seven hours of sleep is associated with up to 10% better performance on tests. You most likely have some flexibility around when you can take the assessment. Use that to your advantage, and choose a time when you’re feeling rested and alert.

• Are you doing something to celebrate? You put a lot of hard work into this! We’re rooting for you. Do something to relax and reward yourself when the assessment is done.  

Good luck, and remember that with the CodeSignal General Coding Framework, you aren’t locked into your score forever — you can retake the test later if you didn’t do your best. 

Want to practice or level up your skills before your next assessment? CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

The post Check off this list before you take a coding assessment appeared first on CodeSignal.

• How you can find a job during the COVID-19 crisis
• Strategies and tips for parlaying your skills into new opportunities

Keeping a cool head in an unprecedented economy

As of May 21, over 38 million people have filed for unemployment in the last nine weeks with U.S. jobless claims expected to grow from April’s 14.7% to 20% or higher. While the impact of COVID-19 may be less severe in the tech sector than in other industries, there are many people who are laid off or furloughed in this unprecedented market. 

It is easy to get overwhelmed, but there are strategies and resources available to accelerate your transition. Here Sloyan and Baik share their Top 3 Tips for finding a job today in reverse order from good to great. Here we go.

#3 – Layoffs list

Trackers such as Layoffs.fyi are a good starting point for real-time industry context to ground your search and as a portal for laid-off startup employees together in one place. The benefit of these crowdsourced spreadsheets, many of which have been published publicly on LinkedIn, is that they make it known you are actively seeking new opportunities with companies hiring for your skillset. It is a double-edged sword, admits Sloyan, because there is a false perception that the people getting laid off are the under-performers in the company and not top talent. While good for getting your name out there and building relationships within your network, this tactic is not a standalone solution for finding your next job.

Source: https://techcrunch.com/2020/05/04/data-shows-which-tech-roles-might-be-most-vulnerable-amid-layoffs/ 

#2 – Recognize skills are highly transferable

While companies may not be hiring for your specific job title, they may be in need of your skillset. “This is a time to be open-minded and think about what skills you have and where you want to apply them in your career,” says Baik. Take a step back and think about the core skills that made you successful in past roles and where there is greatest demand for them today. While you may have to take a temporary step back in title (i.e. moving from a senior to mid-level) when you move into a different role, high potential talent can re-ramp to the level in their previous company through high performance. On top of that, it is good to manage expectations for yourself and count yourself lucky in the aforementioned unprecedented times.

#1 – Target companies that have been insulated from the pandemic’s effects 

With a little thoughtful planning and smart networking, you can find pockets of opportunity in this COVID-19 job market. Browse jobs that are “work from home” or “remote” and explore companies dedicated to collaboration tools, grocery and food delivery, and others in industries experiencing a surge in demand during this time. There are plenty of national lists of companies hiring during the crisis or tech-specific firms hiring now. Explore their job boards to see what skills they are hiring for and which ones are a fit with your unique talents. (When it comes time for the remote interview, CodeSignal is your go-to source, whether you are a candidate or a recruiter adapting to this new normal.) 

Nurture your network

It is always a great time to start building relationships with people you know at your target companies. Bounce back from job loss faster by reaching out to your existing network and using this time as a chance to remember to continually nurture it. The combination of taking a step back to consider your core skill set, researching companies hiring during the pandemic, and engaging with people who can elevate your visibility as a qualified job-seeker are all tangible and smart steps you can take right now.

Level up your skills

CodeSignal Learn is a revolutionary learning product for anyone launching a technical career, pivoting into a new role, building competitive and in-demand skills for a job search, or leveling-up in their current role. Take courses in machine learning, data science, Python programming, and more with one-on-one support from the smartest AI guide in the universe, Cosmo. Sign up to get started for free.

The post Top 3 tips for finding a job today appeared first on CodeSignal.