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Amazon interview notes, page 38 of 159

FAANG / Big Tech

Most of these Amazon write-ups come from Software Engineers (1,092), then Data Scientists (182) and Product Managers (165). The most reported rounds are the technical phone screen (618), the behavioral / leadership round (309), the online assessment (228) and the system design round (159).

1581 interview notes · updated Oct 2026

Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Amazon SWE behavioral round, just one question but they really dug into it. The whole conversation was basically unpacking one leadership principle from every angle.
  • Describe a situation where you had to move a project forward without having all the information you needed. What options did you weigh, what risks did you account for, and how did you know whether it worked out?

“The part that tripped me up was the 'versus waiting' framing at the end.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Amazon with a classic estimation question. Not much else to say, pretty short session from what I remember.
  • How many pairs of shoes does the average person own over their lifetime?

“Blanked for a second on where to even start.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2026
Ran into a promo code validation problem during what looks like an Amazon OA and got stuck trying to work through it with arrays and hash tables. Posting here looking for help, so clearly it didn't go great.
  • Given a set of promo codes, validate which ones meet the required criteria (the 'Promo Code Validator' problem).

“Tried to brute-force my way through it with arrays and a hash table but couldn't land on a clean solution.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Amazon for an Applied Scientist role, pretty much centered on the leadership principles. This one question took up a good chunk of time.
  • Describe a situation where you had to make a quick decision without all the information you wanted. What trade-off did you make between speed and certainty, how did you handle it, and what happened? How did you reduce the risk of acting on incomplete information?

“I had a decent story for this but I fumbled the risk mitigation part.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Amazon coding screen for a software engineer role, one algorithm question the whole time. Pretty standard sliding window stuff but I fumbled explaining my reasoning more than I'd like to admit.
  • Given an array of fruit types on trees, find the maximum number of fruits you can collect if you can only carry two distinct types of fruit at a time (you must pick from consecutive trees and stop when you hit a third type).

“I knew it was a sliding window problem pretty quickly, the constraint of two distinct types makes that obvious.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

May 2026
Amazon PM interview with a product design prompt. You get assigned one of three scenarios on the spot and have to walk through vision, users, features, tech, and metrics. Pretty broad scope for a single question.
  • You're a PM tasked with enabling U.S. books to be sold to Korean customers. Walk through your product vision, target users, key user journeys, MVP features, technical approach, and success metrics.
  • Design an end-to-end web experience for showing customers accurate delivery estimates. Which backend systems would you rely on, and how would you handle latency and edge cases like unexpected delays?
  • Design a next-generation washing machine. What are the core user needs, what features would differentiate it, and how would you bring it to market?

“This one has a lot of moving parts.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral loop for a Data Scientist role at Amazon, three questions deep and pretty much all leadership-principles territory. Nothing technically wild but the 'why Amazon' question tripped me up more than I expected.
  • Describe a time you personally faced a major challenge at work and how you handled it.
  • Tell me about a situation where your team was struggling. How did you step up to help and keep people motivated?
  • Why do you want to work at Amazon specifically?

“I had a decent story ready but fumbled the numbers part.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Technical screen for a Data Scientist role at Amazon covering a pretty broad sweep of ML fundamentals. Nothing too exotic but the breadth meant you couldn't really hide gaps anywhere.
  • How do you handle missing values in a dataset before training a model, and what's your reasoning?
  • Given a business problem, walk through how you'd pick an ML algorithm and justify that choice.
  • Can you explain how Random Forests work in plain language and how they differ from linear regression?
  • What's the difference between overfitting and underfitting, and how do you detect and address each?

“I rattled off the usual stuff: mean/median imputation, indicator columns for missingness, dropping rows if the rate is high enough.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Amazon behavioral round, one question about scope creep and project growth. Pretty short session from what I can tell, nothing too wild.
  • Tell me about a project that started small but grew significantly in scope. How did you manage that growth?

“Classic Amazon leadership angle.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon DS interview that went deep into experimentation stats, way deeper than I expected. The whole session was basically one long A/B testing case with five parts, and I was not prepared for how much math they wanted on the spot.
  • For a two-arm randomized test comparing spam rates of 2.0% vs 1.8%, derive the per-arm sample size needed at alpha=0.05 and 80% power using a two-sided z-test on proportions. State your assumptions about pooled vs unequal variance.
  • Given control=2.1% spam on 500k emails and treatment=1.85% on 500k, compute the 95% confidence interval for the absolute difference in proportions and the corresponding p-value.
  • How do class imbalance drift and traffic seasonality threaten the validity of a spam classifier experiment, and how would you use stratification or blocking to address them?
  • If you peek at results daily and stop the experiment early whenever p<0.05, why does Type I error inflate, and what corrections would you apply?
  • If precision at a fixed recall is a business-critical metric, how would you construct a confidence interval for it using the delta method versus bootstrapping, and when is each approach more appropriate?

“I fumbled the pooled variance part.”

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