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Amazon interview notes, page 144 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 - System Design / Architecture

Jul 2026
Amazon solutions architect interview with a single system design question about session persistence. Pretty focused, no fluff.
  • For a web application, how would you decide between sticky sessions on the load balancer versus storing session state in a database?

“I went straight to the database approach because that's what I'd actually do in production, but I think I undersold the tradeoffs.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Amazon behavioral round for a software engineer role. One question, but it had a lot of moving parts and they clearly wanted a full story, not just a vague answer about working hard under pressure.
  • Tell me about a time you led a project under a tight deadline. Walk through the scope, how you broke the work down, how you prioritized, how you communicated risk and progress to stakeholders, what trade-offs you had to make, and what the outcome was.

“This question is sneakily exhausting because it's not one question, it's like six.”

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Amazon·Software Engineer·Online Assessment (OA)

Jul 2026
Amazon SWE coding round, one question the whole time about log processing. Pretty standard heap problem but they pushed on the parallelization angle which I wasn't fully ready for.
  • Given a log file with page names and their click counts, find the top K pages by click count. How would you approach this efficiently, and how would you handle a very large log file?

“Got the min-heap solution down fine, K-size heap, iterate through logs, swap when you find something bigger.”

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Amazon·Software Engineer·Online Assessment (OA)

Jul 2026
Amazon SWE interview with a tricky inventory pricing problem. The algorithmic setup looked straightforward at first glance but the operation structure made it a lot more subtle than a typical range-compression question.
  • You have an array of product prices and two integers k and d. In one operation you pick two indices x and y, pick a value p between 1 and k, add p to prices[x] and subtract p from prices[y]. Find the minimum number of such operations to make the difference between the max and min price strictly less than d.

“Spent a good chunk of time just staring at the operation definition.”

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Amazon·Technical Program Manager·Hiring Manager Screen

Jul 2026
TPM interview at Amazon with a prioritization question that sounds straightforward but has some real tension baked in. One question, no fluff.
  • Your business users are pushing hard for a new feature, but your engineering team has surfaced three bugs that keep recurring. How do you handle this?

“The part I fumbled was jumping straight to 'we prioritize the bugs' without really making the case.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jul 2026
Amazon PM interview, one behavioral question about bridging technical knowledge and team decisions. Pretty standard loop stuff but this question tripped me up more than I expected.
  • Describe a situation where your technical background influenced a key decision your team made.

“I blanked for a second trying to pick the right story.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jul 2026
Amazon PM loop, two behavioral questions that were pretty much back-to-back. Both leaned hard into leadership principles, which I expected, but the follow-up probing caught me off guard more than once.
  • Describe a situation where you had to deliver results despite significant ambiguity. How did you create enough clarity to move forward?
  • Tell me about a time you strongly disagreed with your manager or a teammate. What did you do, and how did it turn out?

“I had a decent story prepped but fumbled the 'how you reduced ambiguity' part.”

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Amazon·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Amazon MLE behavioral round, focused pretty hard on the ownership and cost-reduction angle. One question, but they clearly wanted a lot of depth on it.
  • Tell me about a time you found and fixed a problem that had a measurable cost impact, such as cloud spend, latency, operational burden, or labeling costs. What did you do, and what were the actual numbers before and after?

“This one has more layers than it looks.”

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Amazon·Data Scientist·Online Assessment (OA)

Jul 2026
Coding round for a Data Scientist role at Amazon. One algorithmic problem, reasonably scoped but the duplicate-handling detail is where people trip up.
  • Given an integer array that may contain duplicate values and a target sum, return all unique pairs of values that add up to the target.

“I went straight for a hashmap and it mostly worked, but I fumbled the duplicate counting part.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jul 2026
Amazon PM behavioral loop, three questions all squarely in the leadership principles territory. They probe hard on follow-ups so having vague stories ready will absolutely sink you.
  • Describe a time you came up with an innovative approach to solve a problem.
  • Tell me about a time you dug deep to find the root cause of a problem.
  • Walk me through a situation where an urgent request landed right before a critical deadline. How did you handle it?

“This one tripped me up because I kept reaching for the flashiest story I had instead of the most concrete one.”

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