A Behind-the-Scenes Look at a Google Software Engineer Mock Interview: Strategies, Solutions, and Success Tips

Written by Massa Medi| April 27, 2025

By Okus, Technical Recruiter at Google

Curious what it’s really like to sit through a technical interview at Google? In this article, we’ll dive deep into a mock interview session, spotlighting two software engineers—one in the role of interviewer, the other as the candidate—working through a classic coding problem. While you won’t get this exact question in your own interview, this scenario showcases the types of challenges Google uses to evaluate candidates for problem-solving ability, technical skill, and communication.

Meet the Participants

The interview kicks off with Okus, a technical recruiter from Google, introducing the exercise. The candidate for this session is Sami, a research scientist at Google Research, who gamely steps into the hot seat. Across the virtual table is Juliana, a software engineer at YouTube, ready to play the role of the interviewer.

“Just as a reminder, you won’t have access to a compiler or IDE during your interview, so practice writing code with Google Docs. Try to do it at home. Write code, make sure your code is tested. Do this process before you come to the interview. And don’t worry about small syntax errors like which substring to use for a given method. Just pick one like start-end or start-length and let your interviewer know.”

The Problem: Finding the Largest Square of Good Land

Juliana: “So Sami, here’s my technical question for you.”

Imagine a farmer looking to maximize the area of good land to farm. The farmer’s land is plotted as a matrix of ones and zeros: ones signify good land, zeros signify bad land. The challenge is to find the largest possible area of good land that can be farmed, with the restriction that the farmed area must form a square—not a rectangle.

“Please help the farmer to find the maximum area of the land they can farm in good land.”

Juliana shares a written version of the problem statement and an example matrix. Sami takes a moment to read and analyze the prompt, starting as most effective candidates do: by seeking clarification.

Clarifying Assumptions: “Square or Rectangle?”

Sami: “Must it be a square, the area they want to farm, or could it be a rectangle?”

Juliana: “For this problem, we only want a square.”

Key Takeaway: Open-ended questions aren't just about the code—they're about clear thinking. Stating and checking your assumptions is crucial, especially as many interview problems at Google are deliberately open to interpretation.

Thinking Aloud: Brute Force Approaches Explained

Sami begins with a "brute force" approach, fully narrating his logic.

Imagine traversing each cell of the input matrix. For each cell:

Start another two loops to examine every possible square originating at that cell.
First verify you can form a 1x1 square. Then try a 2x2, a 3x3, and so on, as long as all the relevant cells contain ones.
Keep track of the biggest square found so far.

Visualizing this, for any index (i, j) in the matrix, Sami describes expanding outward—first to the right, then downward, ensuring every cell in the growing square is a one. If the next expansion reaches a zero, that square size is not possible from that starting cell.

Step-by-Step, the Brute Force in Action:

Start at (i, j). Check if it's a one.
If yes, try expanding to all positions to create a 2x2 square (check cells at (i+1, j), (i, j+1), (i+1, j+1)).
If all are ones, try a 3x3, 4x4, etc., updating the maximum when a larger square is found.
Repeat this for every cell in the matrix.

But what’s the catch? This approach has a computational complexity of O(n⁴)—clearly inefficient for large datasets.

Sami confirms that it's not ideal, and Juliana agrees, nudging him to consider a more efficient method.

Recursion: A Top-Down Perspective

Next, Sami explores a recursive approach. The core insight:

For every cell containing a one, ask recursively, “How large of a square can we generate using the current cell as the top-left corner?”
Ignore (return 0) whenever a zero is encountered.
For a one, recursively call to the right, downward, and diagonal cells (i.e., (i, j+1), (i+1, j), and (i+1, j+1)), and take the minimum of their square sizes.
The base case: for any zero, return 0. For cells on the bottom or right boundary, the largest square is 1 (if they are ones), as they cannot extend further.
The result at each position is 1 + min(down, right, diagonal).

Sami and Juliana collaboratively validate the correctness of this method using diagrams (described verbally in the interview). If three neighbor cells can each form a 3x3 square, their shared “parent” cell can form a 4x4—provided its own value is a one.

Pro Tip: Memoization can be added here to store interim results and avoid repeated computation. This improves time complexity to O(n*m).

Dynamic Programming: The Bottom-Up Method

Finally, Sami describes how to convert the recursive approach into a bottom-up dynamic programming (DP) solution, which is considered optimal for this class of problems.

Initialize a DP matrix of the same size as the input, filled with zeros.
For each cell in the input:
- If the input cell is a zero, update the corresponding DP cell to zero (or leave as zero).
- If the input cell is a one, look at the left (dp[i][j-1]), top (dp[i-1][j]), and top-left diagonal (dp[i-1][j-1]) DP values. The new DP value is 1 + min(left, top, diagonal).
At each step, keep track of the maximum value found in the DP matrix. This represents the side length of the largest square.
At the end, return max_side_length * max_side_length as the area.

Juliana points out an important subtlety: the DP array holds the side length of the largest square at each location, not the area. Since the problem requires area, remember to square the side length before returning the result.

Sami: Should I start programming the dynamic solution and explain as I go?
Juliana: That sounds great!

def largest_square(bin_array: List[List[int]]) -> int:
      if not bin_array or not bin_array[0]:
          return 0
      n, m = len(bin_array), len(bin_array[0])
      dp = [[0] * m for _ in range(n)]
      max_side = 0
      for i in range(n):
          for j in range(m):
              if bin_array[i][j] == 1:
                  if i == 0 or j == 0:
                      dp[i][j] = 1
                  else:
                      dp[i][j] = 1 + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1])
                  max_side = max(max_side, dp[i][j])
      return max_side * max_side

As Sami codes, he talks through every step, considering edge conditions (like cells in the first row or column that can't look “backward”), optimizing to avoid redundant computations, and making the code clear and resilient.

He also discusses optimizations, such as tracking the maximum inside the main loop instead of a separate pass and possible further improvements for cleaner syntax.

What Makes a Strong Google Interview Performance?

This in-depth session illustrates the key characteristics Google interviewers look for:

Communication: Clearly explaining your thought process as you go is just as important as finding the right answer.
Questioning Assumptions: Ask clarifying questions early; open-ended problems require that you define boundaries.
Iterative Improvement: Start with brute force, then discuss efficiency improvements—a path many interviewers want to see.
Attention to Detail: Proactively handle edge cases and be explicit about performance and correctness.

“I hope this gives you a better understanding of what it’s like to answer technical interview questions at Google. As a reminder, be sure to explain your thoughts, get clarity when possible, think through how to improve your answers, and practice ahead of time. Thanks for reading, and good luck on your interview!”

If you'd like to see more real-world examples of system design questions and explore Google's hiring process in detail, check the links below or browse through our technical interview prep series.