Amazon Interview Questions — Page 22

Amazon·Data Scientist·Onsite - Behavioral / Leadership

Apr 2026

Amazon DS behavioral loop, heavy on ownership and accountability. Every question had a 'and what were the measurable outcomes' follow-up baked right in, so vague answers were not going to fly.

Tell me about a time you failed to deliver on an important commitment. Walk through how the commitment was scoped and agreed upon, what warning signs you saw early on, why you still missed it, how you communicated before and after the miss, the actual impact, and what you changed in your process afterward.
Describe a situation where you proactively stepped in and took over a responsibility that wasn't yours. How did you manage your own workload at the same time, what trade-offs did you consciously make, and how did you handle accountability and the eventual handoff?
Give an example where you dug past the obvious explanation to find the real root cause of a problem. What methods did you use, what data did you collect, and why was your first hypothesis wrong?
Tell me about a time you were dissatisfied with part of your team's performance or output. What specifically bothered you, what did you do about it, and what was the concrete result?

“This one is brutal because they want the full chain: how the commitment was formed, who was on the hook, what the early signals were that you ignored.”

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Amazon·Data Scientist·Technical Phone Screen

Apr 2026

Amazon data scientist interview with a single brutal causal inference question about medication reminder rollouts. No randomization, staggered adoption across markets and channels, and they wanted the full treatment: identification strategy, bias handling, modern DID estimators, matching fallback, negative controls, spillovers, and power calculations. It was a lot.

You can't randomize who gets medication subscription reminders because the product launched in a staggered way across markets and channels (push, email, SMS). Outcomes are CSAT (1-5) and 4-week retention, and there's potential household spillover. Design a full observational causal study covering: identification strategy and regression spec with fixed effects and clustered SEs; treatment/risk set definitions to avoid immortal-time bias; pre-trend diagnostics and a modern DID estimator choice; a matching or weighting backup plan; two negative controls and a falsification test; how you'd handle spillovers, missing CSAT, and channel self-selection; and a power/MDE outline.

“This was basically seven questions stapled together and handed to me like it was one thing.”

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Amazon·Software Engineer·Online Assessment (OA)

Apr 2026

Amazon SWE online assessment, second part of the OA, where you get a buggy full-stack repo and an in-browser AI assistant and have 60 minutes to reproduce failures, find the broken code, and get the test suite green. No whiteboard algorithms, just a tight engineering loop under a clock.

Given a buggy full-stack repo (e.g. a loan system where users can't create or view loans, or can fund with insufficient balance), reproduce the failures, locate the broken modules, and patch them so the test suite passes within 60 minutes.
Fix a broken password reset flow: the verification code is never generated, the 30-second expiry isn't enforced, and the new password isn't persisted to the user profile. There's also a separate test URL file with undefined variables that breaks the test runner before you even start.
Implement rate limiting and a keyword/user blocklist on top of an existing repo as a full-stack feature addition.
Debug a return-system fraud scoring service where six numeric thresholds and weighting values are intentionally seeded with wrong values across the scoring path.
Pick one of four backend frameworks (including Django and Spring Boot) before entering the assessment, then fix two seeded bugs in a small CRUD service so the bundled tests pass. You cannot switch frameworks once you've chosen.
Fix a Jira-like comment platform where creating and updating comments doesn't persist or display correctly. The fix must also correctly return 401/403 with specific messages for unauthenticated or unauthorized requests.
Add content moderation to a review system: bad-content checks must block submissions with 403, increment a violation counter, set a flagged state after 3 strikes, keep blocking already-flagged users, and record the specific violated words.
Fix a movie watchlist service where creating, deleting, and updating lists and adding/removing movies are broken due to missing status codes, missing existence checks, and movies not being saved to the database.
Track down a single bug hiding the movie recommendations section on a user's home page, with the constraint that the embedded AI assistant can only see the file you currently have open.

“The loan scenario is probably the most common one people run into.”

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Amazon·Software Engineer·Online Assessment (OA)

Apr 2026

Got an OA from Amazon for a SWE role and the problem was a greedy/DP thing about moving security units left to maximize protected city population. Not the hardest problem I've seen from them but it took me longer than I'd like to admit to nail down the logic.

Given n cities in a line, each with a population value, and an array indicating which cities start with a security unit, find the maximum total protected population. A unit can either stay in its city or move one step left. Each city only needs one unit to be considered protected, extras don't help.

“Spent the first few minutes overcomplicating it.”

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Amazon·Data Scientist·Online Assessment (OA)

Apr 2026

Amazon OA for a Data Scientist role, two algorithmic problems, nothing too wild but the time pressure is real and the edge cases will bite you if you're not careful.

Given an array of integers and a target value, return the indices of the two numbers that sum to the target. Assume exactly one valid solution exists.
Given a string, find the length of the longest substring that contains no repeating characters.

“Classic two-sum.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at Amazon for a SWE role. Just the one question from what I can tell, classic leadership principle territory.

Tell me about a situation where, looking back, you would have handled things differently.

“I went with a safe story and I think that was the wrong call.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Amazon system design round, just the one question about firmware update delivery. Pretty open-ended and I don't think I scoped it well enough before diving in.

Design a system that delivers firmware updates to devices at scale.

“I jumped straight into the delivery pipeline without thinking about the device side first.”

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Amazon·Software Engineer·Hiring Manager Screen

Apr 2026

Interviewed for a business analyst role at Amazon, one round focused on how you translate data insights into something the business can actually act on. Pretty standard stuff but the question had more depth than I expected.

How do you make sure your data-driven recommendations are actionable and tied to actual business goals?

“I fumbled this a bit because I went straight into talking about dashboards and metrics, which missed the point entirely.”

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Amazon·Software Engineer·Online Assessment (OA)

Apr 2026

Amazon OA for a SWE role, one algorithmic problem about price normalization. Pretty standard online assessment format, nothing too surprising except the problem had some edge cases I didn't fully think through at first.

Given an array of product prices, a max step size k, and a target gap d, find the minimum number of operations to make the difference between the max and min price strictly less than d. Each operation lets you pick two products and shift value p (1 <= p <= k) from one to the other.

“My first instinct was greedy, just keep pulling the extremes toward each other.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Amazon SWE behavioral round, two questions back to back, both leadership principle heavy. The whole thing felt like they wanted a paper trail of your decision-making, not just a story.

Tell me about a time you worked under a tight deadline. How did you break down the work, prioritize, negotiate scope, and communicate trade-offs to stakeholders? What was the result?
Describe a time you missed a commitment. What caused it, how did you handle the fallout with stakeholders, and what did you put in place so it wouldn't happen again?

“The part that tripped me up was the scope negotiation angle.”

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