Google Interview Questions — Page 210

Google·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Google system design round for a software engineering role. The whole question was basically one big prompt covering a lot of ground, which was both nice (clear scope) and stressful (no obvious place to start).

Design a service that tracks each user's watched video history and lets them resume playback from where they left off. Cover the API design, data model, idempotent progress updates, cross-device sync, privacy controls, and how you'd handle read and write scale.

“This was a lot to hold in your head at once.”

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Google·Data Scientist·Technical Phone Screen

Jul 2026

Google data scientist interview that went deep into a single stats/ML problem. The whole session was basically one extended question about a custom error metric, bootstrapping, and probability theory. More math-heavy than I expected for a DS role.

Given a CSV with country, actual revenue, and predicted revenue columns, implement a percentage RMSE metric defined as the square root of the mean squared relative error. Your implementation needs to handle zeros and negative values, support optional country-level weights, and be numerically stable.
Implement a nonparametric bootstrap with resample size equal to n to get a 95% confidence interval for the pRMSE. Justify why you use n as the resample size, and explain when you'd prefer a stratified or cluster bootstrap instead.
What is the exact probability that a single bootstrap resample, drawn with replacement from n observations, produces the exact same sample in the exact same order as the original? Give the formula in terms of n and explain why this probability is negligible in practice.
What are the limitations of using bootstrapping to estimate uncertainty for this percentage RMSE metric, specifically around heavy-tailed distributions and cross-country dependence? What mitigations would you consider?

“This tripped me up more than I wanted to admit.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at Google for a software engineer role, centered entirely on designing a calendar product at scale. The conflict detection piece was where things got real fast.

Design the event schema and settings for a large-scale calendar product. What fields does an event need, and how do you decide whether to use one schema for everything or separate read and write paths?
How does your system detect scheduling conflicts when a user creates or updates an event? Walk through the exact predicate you use for time-range overlap and how you index for it.
When checking conflicts for a multi-attendee event, do you block on any attendee's conflict, only required attendees, or just the organizer? How does your system enforce whichever policy is chosen?
How do you handle conflict detection for recurring events, especially when the recurrence is unbounded (e.g., every weekday forever)?
How do you store and reason about time zones in your event model, particularly for recurring events that cross a daylight saving time change?
How do you handle concurrent edits to the same calendar or event? What concurrency strategy do you use and why?
How do you expose conflict information to users who don't have full visibility into another calendar? How do you return busy-block data without leaking private event details?
How do you keep a denormalized availability index consistent with the canonical event store, and how would you detect and repair drift between them?
How would you add a 'find a time that works for all attendees' feature, and how does the cost scale with the number of attendees and their time zones?
If double-booking were strictly forbidden for certain resources (like a conference room), how would your design change compared to the advisory conflict model for regular users?

“I started listing fields (title, attendees, time zone, recurrence, reminders, location, video link) and the interviewer let me go for a bit before asking what the conflict check actually needs from all that.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026

Behavioral round at Google for a software engineer role, focused entirely on conflict and how you navigate disagreements with peers, managers, or cross-functional folks. One question, but they really dig into it.

Tell me about a serious conflict you had at work, whether with a peer engineer, your manager, or a cross-functional stakeholder like a PM or SRE. Walk me through what each side wanted, how you handled it, and what came out of it.

“I had a story ready but underestimated how deep they'd push on the 'what would you do differently' part.”

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Google·Software Engineer·Technical Phone Screen

Jul 2026

Went through Round 1 at Google and came out more confused than relieved. The coding question felt way too easy for what I'd been preparing for, and my next round has been rescheduled three times now due to interviewer availability issues.

Implement a preorder traversal of a binary tree.

“I spent weeks grinding hard problems and they gave me this.”

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Google·Software Engineer·Onsite - Multi Round

Jul 2026

Two rounds of coding interviews for a Google SWE intern role, and I genuinely can't tell if I passed or embarrassed myself. Both rounds had bugs, missed edge cases, and follow-ups I only half-answered. Now I'm just waiting and spiraling.

Solve a medium-to-hard algorithmic problem that builds on a simpler warm-up by adding an extra constraint.
Given an algorithmic problem, identify the optimal approach and implement a working solution, then propose an alternative approach as a follow-up.

“My first approach worked but was clearly not optimal.”

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Google·Software Engineer·Technical Phone Screen

Jul 2026

Google SWE coding round, one question the whole time. They wanted me to implement Fisher-Yates from scratch using a custom random API and actually justify the uniformity, not just write the code.

Given an array of n distinct elements and a rand_int(l, r) API that returns a uniform random integer in [l, r], implement an in-place shuffle such that every permutation is equally likely. Describe the algorithm, prove it produces a uniform distribution, and give time and space complexity.

“I knew Fisher-Yates going in, so the code part was fine.”

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Google·Product Manager·Onsite - Behavioral / Leadership

Jul 2026

Google PM interview, one question that sounds deceptively simple until you're actually in the room trying to explain a technical project to someone who doesn't care about your architecture decisions.

Walk me through a recent project you led, explaining the problem you were solving, how you approached it, and what the outcome was, as if you're talking to someone with no technical background.

“I went straight into solution mode and caught myself two sentences in using terms nobody outside my team would know.”

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Google·Data Scientist·Technical Phone Screen

Jul 2026

Google Data Scientist technical screen, two fairly meaty coding problems back to back. The questions were more software-engineering-heavy than I expected for a DS role, lots of Unicode edge cases and streaming constraints that I hadn't really prepped for.

Write an anagram-checking function that handles Unicode normalization, ignores case/whitespace/punctuation, strips combining marks for English locales, and runs in O(n) time with O(alphabet size) extra memory without materializing the full normalized string.
Follow-up: how would you adapt the anagram checker to handle locale-specific rules like German 'ß' expanding to 'ss', and how do you handle right-to-left scripts without breaking normalization?
Implement a lazy, stable de-duplication generator that yields the first occurrence of each distinct element, supports a custom key function, and optionally treats all NaN floats as equal, in O(n) time and O(distinct keys) space.

“The streaming constraint is what tripped me up.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at Google for a software engineering role. The whole session was basically one big rate limiter question that kept expanding into distributed systems territory, which I was not fully prepared for.

Design a rate limiter that enforces a maximum QPS limit on API calls, with support for optional burst allowance. Compare two approaches like token bucket and sliding window, covering data structures, time and space complexity, and correctness under concurrent access.
Extend your rate limiter design to a distributed environment running across multiple application instances. How do you handle coordination, clock skew, atomicity, idempotency, and failure modes like node loss or partial updates?
What APIs, configuration options, and monitoring or alerting mechanisms would you expose for this rate limiter system, particularly around detecting saturation?

“I started with token bucket because it felt safer, explained the refill logic, talked about using a deque for sliding window.”

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