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Amazon interview notes, page 137 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·Online Assessment (OA)

Jun 2026
Amazon SWE online assessment, one coding problem that looked like a standard ML implementation task but had enough edge cases to slow me down.
  • Implement a linear regression model from scratch using backpropagation to iteratively update weights and bias. Given a 2D feature matrix X, a label array y, a learning rate, and an epoch count, return the final predictions along with the trained weights and bias.

“I knew the math going in but writing it cleanly under time pressure was a different story.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Did a security analyst screen at Amazon, pretty standard stuff but the foundational questions tripped me up more than I expected.
  • Can you explain the differences between a vulnerability, a threat, and a risk?

“Thought I had this cold but mid-answer I started second-guessing myself on where threat ends and risk begins.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon behavioral screen for a software engineer role, six leadership principle prompts back to back. Prepare distinct stories for each because they will notice if you recycle the same project twice.
  • Tell me about a time you worked under a tight deadline. How did you plan and prioritize, what trade-offs did you make, and what was the result?
  • Describe a time you missed a deadline. What happened, how did you communicate it, and what did you take away from it?
  • Walk me through a piece of negative feedback you received. How did you react, what did you change, and did you follow up afterward?
  • Give an example of when you went beyond what was asked to deliver extra value. Why did you do it, how, and what measurable impact did it have?
  • Describe a situation where you had to dig deep into the details to diagnose a problem. How did you form hypotheses, gather data, and confirm the root cause?
  • What two questions would you ask the hiring manager about role expectations and how the team actually operates?

“I had a story ready but fumbled explaining the trade-offs part.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026
Amazon SWE coding round with a greedy/sorting problem around minimizing total packaging effort across fulfillment centers. The problem had a neat twist with the free item bonus mechanic that made it less obvious than it first looked.
  • Given a list of item packaging efforts and a list of fulfillment center thresholds, where paying to package at least a threshold number of items at a center gives you 2 free items (each with effort at most the minimum paid effort at that center), return the minimum total packaging effort to package all items. Each center can be used at most once.

“The free items mechanic is what makes this tricky.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon solutions architect interview, just one question about networking basics. Not much to go on but it felt like a technical screen.
  • What is CIDR?

“Pretty fundamental networking question.”

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Amazon·Data Scientist·Technical Phone Screen

Jun 2026
Amazon Data Scientist technical screen, one main coding problem with a follow-up that caught me a bit flat-footed. Classic sliding window territory but the extension to collecting all qualifying substrings added some pressure.
  • Write a Python function that returns the length of the longest substring without any repeating characters. Then, modify it to return all substrings that actually achieve that maximum length.

“Got the first part pretty cleanly, sliding window with a dict tracking last-seen indices, nothing too painful.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE coding round, pretty much just one graph problem the whole time. Standard topological sort setup but they did ask about complexity at the end which I half-fumbled.
  • Given n tasks and a list of dependency pairs where each pair indicates one task must complete before another, return a valid ordering of all tasks. If the dependency graph contains a cycle, return an empty list. Also discuss time and space complexity.

“Recognized it as topological sort pretty fast, which was good.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE coding round focused on a sentence/word indexing problem. Pretty design-heavy for what I expected to be a straightforward coding question.
  • Design a data structure that indexes words to the sentences they appear in. You should be able to look up all sentences containing a given word, and also delete a sentence while keeping the index consistent.

“Spent the first few minutes thinking it was just an inverted index problem and almost rushed straight to a hashmap of word to sentence list.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE interview that went deep on utility function implementation. The whole session was basically one long question about debounce and throttle, and they kept pushing on edge cases until I ran out of things to say.
  • Implement a debounce function in JavaScript that supports leading and trailing invocation options, cancellation, and an immediate flush method.
  • Implement a throttle function in JavaScript with leading/trailing toggles, a maxWait option, cancellation, and flush support.
  • How would you handle edge cases like timer drift, re-entrancy, and error propagation in your debounce/throttle implementations?
  • What is the time and space complexity of your debounce and throttle implementations, and how would you test them?

“I got the basic version out fast, but the leading/trailing toggle tripped me up.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026
Amazon Data Scientist interview with a Tableau-heavy technical round. Three questions, all centered on dashboard design, filter mechanics, and data viz principles. Felt more like a BI engineering interview than a data science one, which threw me off a bit.
  • What are the key differences between a JOIN, a BLEND, and a RELATIONSHIP in Tableau, and when would you use each?
  • Tableau has six filter types applied in a specific order of operations. Can you walk through that order and describe a real scenario where using the wrong filter level produces incorrect results?
  • A stakeholder wants to use a pie chart to display 12 product categories. What would you recommend instead and why?

“I knew joins cold but blends tripped me up a little.”

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