4 Secrets to Success in FAANG Coding Interviews

Introduction

If you’re gearing up for a technical interview, especially with top tech companies like those in the FAANG group, then you’re in the right place. In this article, we’ll explore what these tech giants look for in candidates and how to ace coding problems. You’ll learn how to clarify the problem, suggest a brute-force solution, and ultimately arrive at an optimal and efficient solution — all within the tight time frame of a tech interview.

What Are Companies Looking For?

First, let’s understand the four primary areas that companies assess during technical interviews:

1. Coding Skills: The most obvious axis of evaluation is your ability to write code. The emphasis here is on clean, readable, and production-quality code, regardless of whether your solution is correct.

2. Analytic Skills: Here, interviewers assess your capacity to understand the problem, ask clarifying questions, propose solutions, and analyze the time and space complexity of your solutions.

3. Communication Skills: The ability to articulate your thought process as you code is vital. Good communication helps your interviewer understand how you approach problems, from the unknown to the solution.

4. Data Structures & Algorithms: You need a solid grasp of DS & A to formulate effective solutions. Selecting the right data structure for the problem is often the most challenging part.

Preparing for Coding Interviews

Before diving into solving coding problems, you need a foundational understanding of Data Structures and Algorithms. You can generally acquire this knowledge in a month or two of focused study. Alongside tutorials and courses, practice coding problems on platforms like LeetCode to identify patterns and familiarize yourself with different kinds of questions.

A Practical Guide to Solving Coding Problems

Instead of just listing out steps, let’s walk through a sample interview coding problem to show you what you should do to impress your interviewer.

The Problem

You’re given two arrays. Create a function that informs the user whether these arrays have any items in common.

// Example 1
const array1 = ['a', 'b', 'c', 'x'];
const array2 = ['z', 'y', 'i'];
// Should return false

// Example 2
const array1 = ['a', 'b', 'c', 'x'];
const array2 = ['z', 'y', 'x'];
// Should return true

Step 1: Asking Clarifying Questions (Communication Part)

1. Repeat the Problem: Clarify the question by repeating it in your own words and offering a few examples.

2. Confirm Inputs and Outputs: Make sure you understand the data types of the input and output. For instance, can the arrays contain both strings and integers? Should the output be a boolean?

3. Clarify the Goal: Understand whether you should optimize for time or space complexity.

4. Input Size: Get a sense of how large the input arrays can be. This can influence your choice of algorithm.

Step 2: Suggesting Solutions (Analysis Part)

After understanding the problem, it’s time to propose possible solutions.

1. Brute-Force Solution: Initially propose a brute-force solution and discuss its time and space complexities. For instance, nested loops comparing each element would yield a time complexity of O(a * b) and a space complexity of O(1).

function containsCommonItem(arr1, arr2) {
  for (let i = 0; i < arr1.length; i++) {
    for (let j = 0; j < arr2.length; j++) {
      if (arr1[i] === arr2[j]) {
        return true;
      }
    }
  }
  return false;
}

2. Optimized Solution: Subsequently, discuss an optimized solution. For instance, converting the first array into a hash map and iterating over the second array to look for common items would yield time and space complexities of O(a + b) and O(a), respectively.

function containsCommonItemOptimal(arr1, arr2) {
  const map = {};
  for (let i = 0; i < arr1.length; i++) {
    map[arr1[i]] = true;
  }
  for (let j = 0; j < arr2.length; j++) {
    if (map[arr2[j]]) {
      return true;
    }
  }
  return false;
}

Step 3: Coding

Once your solution is approved, it’s time to implement it:

• Create a class called Solution to organize your code.

• Work quickly but effectively, and take care of edge cases.

• If stuck, communicate your thought process with the interviewer.

Step 4: Testing Your Code

Finally, make sure to:

• Run tests on your code with the provided example and other edge cases.

• But don’t run too many test cases unless specifically asked.

Conclusion

By systematically approaching the problem in this manner, you’ll earn high scores across all four evaluation criteria, putting you in a strong position to ace your technical interviews.

If you’re interested in diving deeper into how to analyze the time and space complexities of your solutions, don’t miss my guide on Big O notation.

Good luck with your interviews!