I recently completed the latest Google Online Assessment and wanted to share my experience. The June 2026 Google OA included two algorithm problems, both of which I finished in around 25 minutes with full acceptance. The difficulty wasn't overwhelming, but both questions were classic examples where a straightforward brute-force solution would almost certainly exceed the time limit. Instead, they required recognizing optimization patterns commonly seen in LeetCode Medium problems.
If you're preparing for Google OA, reviewing these question styles and understanding the optimization ideas behind them can significantly improve your chances of passing.
Google OA Structure
The assessment consisted of two coding questions with approximately 70 minutes to complete them on HackerRank.
One thing that consistently stands out in Google OA is the emphasis on edge cases. Empty matrices, single-element arrays, arrays containing all positive or all negative values, and other boundary conditions frequently determine whether your solution passes all test cases. Even if your main algorithm is correct, failing to handle these special cases can easily cost valuable points.
Before submitting, always spend a few minutes verifying edge cases manually.
Question 1: Count Comparisons
Problem
Given an N Γ D matrix, compare every pair of rows and count the total number of element comparisons performed while determining whether each pair of rows is identical.
Rows are compared from left to right. Comparison stops immediately once two elements differ or after all D elements have been compared.
Solution Idea
The most obvious solution compares every pair of rows directly, resulting in a time complexity of O(NΒ² Γ D). While easy to implement, this approach becomes too slow for larger inputs.
A much better strategy is to sort all rows (treating each row as a tuple) and then group rows according to shared prefixes. By processing comparisons column by column within these groups, you can efficiently count how many comparisons occur before rows diverge. Completely identical rows contribute exactly D comparisons for each matching pair.
The key optimization is replacing brute-force pairwise comparisons with sorting and grouped counting, dramatically reducing redundant work.
Question 2: Reversed Subarray Sum
Problem
Given an array A of length N, you must reverse exactly one subarray [l, r]. Determine the maximum possible subarray sum after performing the reversal.
Solution Idea
The answer comes from considering two possibilities.
- The optimal subarray remains unchanged after the reversal.
- The reversal creates a better subarray by connecting a strong prefix segment with a strong suffix segment.
To solve this efficiently, preprocess two arrays:
- prefix_max[i]: the maximum subarray sum ending at position i.
- suffix_max[j]: the maximum subarray sum starting at position j.
Then enumerate every possible split where i < j and compute:
prefix_max[i] + suffix_max[j]
The maximum value obtained is compared with the original maximum subarray sum, and the larger result becomes the final answer.
The core optimization lies in preprocessing prefix and suffix maximum subarray sums instead of explicitly simulating every possible reversal.
Preparation Tips
One common pattern across Google OA problems is that the brute-force solution is usually easy to discover, but it almost never satisfies the required time complexity.
Instead of jumping directly into coding, spend the first three to five minutes estimating the complexity of your initial idea. If it looks like O(NΒ²) or worse, pause and consider whether sorting, hashing, preprocessing, prefix sums, dynamic programming, or other optimization techniques can reduce the complexity.
For this assessment:
- Question 1 replaced exhaustive pairwise comparisons with sorting and grouped processing.
- Question 2 replaced exhaustive reversal simulation with prefix/suffix preprocessing.
Recognizing these optimization opportunities is often the difference between passing and timing out.
My Experience
Before taking the assessment, I prepared with help from InterviewShow. Google OA optimization problems can be challenging when you encounter them for the first time because it's easy to get stuck on a brute-force approach that seems correct but ultimately exceeds the time limit.
Working through similar optimization patterns beforehand made it much easier to identify the intended solution during the actual assessment.
Final Thoughts
Google OA is often less about implementing complicated algorithms and more about recognizing when brute force isn't enough. Understanding optimization techniques, practicing common LeetCode Medium patterns, and paying close attention to edge cases can significantly improve your performance.
If you're preparing for an upcoming Google OA and want additional guidance, InterviewShow offers OA preparation and interview support to help candidates work through optimization-heavy coding problems and improve their confidence before the assessment.
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