Google Interview Questions — Page 207

Google·Software Engineer·Technical Phone Screen

Jul 2026

Ran into a Google SWE coding problem that boils down to the classic difference array pattern. Not much context given about the round itself but the problem structure is pretty recognizable if you've seen range update problems before.

Given an array and a series of range update operations where each operation adds a value to every element between two indices, return the final array after all operations are applied.

“The brute force is obvious but dies on large inputs.”

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Google·Software Engineer·Technical Phone Screen

Jul 2026

Google SWE coding round with a list manipulation problem that kept growing in scope. The follow-ups were where things got interesting, or stressful depending on how you look at it.

You're given two lists and an integer k. Remove elements from the second list so that its first k elements have no values in common with the first k elements of the first list. Then optimize to O(n) time. Then generalize: given a list of lists, an integer k, and an integer d, ensure that for each list, its first k elements share no values with the first k elements of any of the d lists that came before it.

“The base case felt manageable.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026

Conversational round at Google for a software engineering role, no coding involved. The focus was on personal projects, past internship work, and where you see yourself going career-wise.

Walk me through a personal project you've worked on in depth.
What did you own and deliver during your internship?
Where do you see your career going over the next few years?
Why do you want to work at Google specifically?

“This is where I probably lost the most ground.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Two Google SWE coding rounds, mixed results. First went pretty clean, second got messy toward the end with a debugging spiral and no time left to cover complexity. Now sitting here wondering if that's enough to skip a third round or if I'm about to get the 'additional interview' email.

Solve a coding problem and explain the time and space complexity of your solution.
Given a second coding problem in the same session, implement a solution under time pressure.
Implement a solution to a coding problem, optimizing data structure choices as you go.

“Finished this one with time to spare, walked through complexity, felt good.”

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Google·Data Scientist·Technical Phone Screen

Jul 2026

Heavy stats interview at Google for a DS role. The whole thing was basically one long question about what happens when you duplicate your dataset, and it went deeper than I expected into chi-square tests and Bayesian remedies by the end.

If you duplicate your entire dataset k times and refit OLS, prove algebraically that the coefficient estimates stay the same but the standard errors shrink by 1/sqrt(k). Then walk through the downstream effects on t-statistics, p-values, and confidence interval widths.
Concretely: if the original SE for a coefficient is 0.20 and t is 5.0, and you incorrectly treat 4 duplicates as independent observations, what are the new SE, t-statistic, and approximate two-sided p-value? Show your work.
What does a p-value actually mean, and why does dataset duplication break its assumptions? Give examples of real analyst workflows like oversampling, data augmentation, or bootstrapping that can accidentally produce the same problem.
With chi-square tests, very large samples can produce tiny p-values for effects that are practically meaningless. How would you address this? Discuss reporting an effect size like Cramér's V or an odds ratio with a confidence interval, and also a penalized or Bayesian approach. When is each preferable?

“I knew the punchline intuitively but fumbled the algebra mid-explanation.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Google SWE coding round with a classic dynamic programming problem on envelope nesting. Nothing too surprising if you've seen LIS before, but the same-width edge case is where people slip up.

Given n envelopes each with a width and height, find the maximum number of envelopes you can nest inside each other, where one envelope fits inside another only if both its width and height are strictly smaller.

“The core insight is sorting by width ascending and then running longest increasing subsequence on heights.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Google SWE coding round, one problem the whole session. Dynamic programming on a binary matrix, which I should have been more ready for.

Given a binary matrix of size m x n, count the total number of square submatrices where every element is 1, across all possible square sizes.

“Took me an embarrassingly long time to get past the brute force instinct.”

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Google·Data Scientist·Technical Phone Screen

Jul 2026

Tough technical screen for a DS role at Google. Six questions, all ML heavy, and they really wanted you to go deep on the math and tradeoffs rather than just name-drop algorithms. Left feeling like I'd passed maybe four of the six cleanly.

Define binary logistic regression from scratch: write out the probability model, derive the log-loss and its gradients with respect to the weights and bias, and explain why the loss function is convex and what that means for optimization.
Compare L1 and L2 regularization in logistic regression across sparsity, multicollinearity, decision boundary geometry, and probability calibration. When does elastic net make more sense than using either one alone?
Under what conditions does logistic regression tend to outperform a random forest? Think about decision boundary linearity, high-dimensional sparse features, small sample sizes with many features, and situations where calibrated probabilities matter.
Your model is overfitting. Walk through concrete remedies specific to logistic regression, random forests, and gradient boosting separately. Also describe how you'd detect overfitting beyond just looking at accuracy.
Contrast random forests and gradient boosting across bias-variance tradeoffs, sensitivity to noisy features, hyperparameter sensitivity, handling of missing values natively, and training versus inference cost. Give a real-world scenario where each clearly beats the other.
Case study: you have 50k rows, 10k sparse binary features, 1% positive class rate, and significant temporal drift in the data. Design an end-to-end pipeline for both a logistic regression with elastic net and a tree-based model. Cover feature processing, regularization choices, time-based cross-validation, threshold selection, probability calibration, and how you'd fairly compare the two approaches. Call out the specific pitfalls you'd avoid.

“Felt okay on the model definition and gradient derivation but stumbled a bit explaining convexity in a way that felt satisfying.”

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Google·Data Scientist·Onsite - System Design / Architecture

Jul 2026

System design round at Google for a data scientist role, centered entirely on one meaty ML/retrieval problem. The question had a lot of moving parts and I felt like I was barely keeping up with the scope.

Given a company like Coca-Cola as a starting point, design a system that retrieves the top 20 most similar companies globally for sales prospecting purposes.
How would you handle negative sampling and blocking to make this system scale to 10 million companies, including cold-start scenarios for newly added companies?
Walk through how you would evaluate this system both offline and online, including how you'd measure sales conversion lift and guard against data leakage from subsidiaries or parent companies.

“This question is basically five questions duct-taped together.”

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Google·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026

Behavioral loop for a Data Scientist role at Google. Three big questions, all structured around ownership and impact. Felt more like a research defense than a typical interview.

Tell me about the most memorable project you've worked on. What was the problem, who did it affect, and what did you personally own from start to finish?
Describe the most challenging research problem you've tackled. What made it hard, how did you pick your approach, and what did you change about how you work because of it?
Walk me through a time you pursued a goal with real uncertainty around it. How did you break it down, stay on track, and handle things when they went sideways?

“This one felt open enough that I rambled for a bit before landing anywhere useful.”

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