Mastering Google Coding Interview: Insider Tips from Ben Awad

Table of Contents

1. Introduction
2. Mock Google Coding Interview with Ben Awad
3. Understanding the Problem
4. Developing a Solution Approach
5. Implementing the Solution
6. Testing and Debugging
7. Optimizing the Solution
8. Time and Space Complexity Analysis
9. Conclusion

Introduction

In this article, we will explore a mock Google coding interview conducted by Clement and Ben Awad. The interview focuses on solving a medium difficulty coding problem related to image processing. The problem involves removing disconnected black pixels from a black and white image represented as a matrix. We will discuss the step-by-step thought process, the solution approach, the implementation, testing, debugging, and optimization of the code. Lastly, we will analyze the time and space complexity of the solution. So let's dive in and unravel the intricacies of this coding interview!

Mock Google Coding Interview with Ben Awad

Clement and Ben Awad join forces once again for a simulated Google coding interview. This time, the problem is of medium difficulty, as the previous one was relatively easy. Ben expresses his nervousness due to not having worked with algorithms for a couple of months, but both Clement and Ben agree that it will be a valuable learning experience. They start the interview using the AlgoExpert mock interview workspace user interface. They have 45 minutes to solve the problem, and they begin by understanding the problem statement.

Understanding the Problem

The problem revolves around a black and white image represented as a matrix. The matrix consists of pixels that can be either black (represented by 1) or white (represented by 0). The task is to remove all the black pixels that are not connected to the border of the image. Two pixels are considered connected if they are horizontally or vertically adjacent. The function to be implemented is called "removeIslands," as the disconnected black pixels are referred to as islands. The goal is to remove these islands and return the modified matrix.

Ben clarifies his understanding of the problem by asking questions and discussing the sample input and output provided by Clement. After gaining a clear understanding, they move on to developing an approach to solve the problem.

Developing a Solution Approach

To solve the problem, Ben muses over various approaches. Initially, he considers a brute-force method where he iterates over the outer, middle, and inner rings of the matrix to identify the black pixels connected to the border. However, he realizes that this approach would be too slow for larger matrices. He then suggests working with a graph representation to identify the connected pixels and decides to build an adjacency list for the black pixels.

Clement highlights that the matrix itself can be considered a graph, and they can traverse through its neighbors using a depth-first search (DFS) algorithm. This simplifies the approach, as they don't need to explicitly Create an adjacency list. Ben agrees with Clement and decides to use recursion and DFS to solve the problem.

Implementing the Solution

Ben starts implementing the solution using Python. He sets up the initial code structure and defines the "removeIslands" function that will solve the problem. He also defines Helper functions such as "isBorder" and "borderIslandKey" to check if a pixel is on the border and generate keys for the border islands dictionary.

He then proceeds with implementing the main logic. He iterates over each pixel in the matrix and checks if it is a black pixel. If it is, he checks if it is on the border using the "isBorder" function. If it is on the border, he adds it to the "borderIslands" dictionary with a key generated using the "borderIslandKey" function. He then recursively explores the neighboring pixels to find all connected black pixels. The recursive function is called "recurse" and performs a DFS on the neighbors.

Testing and Debugging

To ensure the correctness of the code, Ben runs several test cases using different sample inputs. He compares the output with the expected output and makes adjustments to the code as needed. While debugging, Ben discovers a bug in the recursion logic that is causing an infinite loop. He realizes that he needs to check if a pixel is already in the "borderIslands" dictionary before recursing on it.

Optimizing the Solution

After successfully debugging the code, Ben suggests optimizing the solution. The Current solution has a time complexity of O(N^2) as it involves iterating through the entire matrix and then recursively exploring each connected black pixel. However, since the recursion is concurrent and bottoms out fairly quickly, the actual time complexity is closer to O(N).

Ben and Clement discuss the possibility of further optimizing the solution. They conclude that due to the nature of the problem, there is no significant room for improvement in terms of time complexity. However, they acknowledge that the code can be cleaned up and made more readable by removing unnecessary comments and simplifying certain parts.

Time and Space Complexity Analysis

The time complexity of the solution is O(N), where N represents the total number of pixels in the matrix. This analysis is Based on the fact that each pixel is visited only once during the DFS traversal. The space complexity is also O(N) due to the recursive nature of the solution, which can Consume additional stack space proportional to the number of recursive calls made.

Conclusion

In this mock Google coding interview, Clement and Ben Awad tackled a medium difficulty coding problem related to image processing. They successfully implemented a solution using a depth-first search algorithm to remove disconnected black pixels from a black and white image represented as a matrix. Through their step-by-step thought process, coding, testing, and debugging, they demonstrated their problem-solving skills and ability to collaborate effectively. They also discussed the time and space complexity of the solution and explored opportunities for optimization. Overall, it was a valuable learning experience that highlighted the importance of clear communication, logical thinking, and adaptability in coding interviews.

FAQ

Q: What is the main goal of the problem discussed in the mock Google coding interview? A: The main goal is to remove all the disconnected black pixels (islands) from a black and white image represented as a matrix, leaving only the black pixels connected to the border.

Q: What algorithm was used to solve the problem? A: The solution involves using a depth-first search (DFS) algorithm to explore the black pixels in the matrix and identify the connected ones.

Q: How was the matrix represented in the code? A: The matrix was represented as a two-dimensional array, where each cell contains either a 0 (representing a white pixel) or a 1 (representing a black pixel).

Q: What was the time and space complexity of the solution? A: The time complexity of the solution is O(N) and the space complexity is also O(N), where N represents the total number of pixels in the matrix.

Q: Can the solution be further optimized? A: The current solution is already optimized, given the nature of the problem. However, code readability improvements can be made by removing unnecessary comments and simplifying certain parts.