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Amazon Interview Questions

Reconstructed from 542 verified candidate reports across 57 questions. Oct 2025 – Oct 2026.

This page is a live view of every Amazon interview question AceOffer has indexed — pulled from real candidate reports, not invented from job descriptions or one founder’s memory. Every question shows how many times it’s been reported and when it was last seen. The catalog gets a refresh pass every month.

542
candidate reports
57
distinct questions
6
round types
Monthly
refresh cadence

Key facts

  • •57 distinct Amazon interview questions indexed
  • •542 candidate reports across the catalog
  • •Most reported: Deliver Results: Tight Deadlines and Missed Commitments — 44× (last seen September 2026)
  • •Reports span Oct 2025 – Oct 2026
  • •Refreshed monthly · last updated October 2026

Browse Amazon interviews by topic

Coding Interview QuestionsOnline Assessment (OA) QuestionsSystem Design Interview QuestionsBehavioral Interview QuestionsMachine Learning Interview QuestionsInterview Process & Rounds

The Amazon loop, from candidate reports

Amazon's process starts with an online assessment. Since January 2026 the coding section is often two tasks in about 100 minutes: an algorithm problem in roughly 40 minutes, then about 60 minutes of debugging an existing repository with an AI assistant that will point at files but will not write the fix. Reported repositories include a movie database app and a Jira-like issue tracker. Some candidates still get two algorithm questions instead. The assessment also has a work simulation, where you rate replies to workplace scenarios, and a work-style survey. Phone screens and onsite coding rounds are mostly LeetCode-style problems and Amazon reskins of them: course schedule and dependency ordering, top-k frequent elements, LRU cache, lowest common ancestor and interval problems. Design rounds are often object-oriented design (Amazon Locker, pizza ordering, a shopping cart and checkout) and sometimes distributed design. Every round also asks Leadership Principle questions, and the bar raiser can spend most of an hour on them; many loops now also ask how you use generative AI, and some ask how you check its output. Applied Scientist loops add an ML breadth screen and ML system design.

What does Amazon ask in each interview round?

Amazon interviews span 6 distinct round types, broken down below. Counts reflect distinct questions per round, not number of times asked. Frequencies on individual question cards show how many candidates reported getting that specific question.

LeetCode-style problems and Amazon reskins: course schedule and dependency ordering, top-k frequent, LRU cache, lowest common ancestor, meeting rooms, union-find, islands, plus a Linux find implementation and a rate limiter.
20 questions

Most-reported: Course Schedule and Dependency Ordering (Topological Sort) (16 reports)
Leadership Principle questions in every round, with the bar raiser often spending most of an hour on them: tight deadlines, using generative AI, conflict and disagreement, ownership beyond scope, dive deep, critical feedback and failures.
13 questions

Most-reported: Deliver Results: Tight Deadlines and Missed Commitments (44 reports)
Often two coding tasks in about 100 minutes: an algorithm problem (drone routing around a circular hub, placing a delivery center on a grid, the most money in K consecutive bags), then debugging a repository with an AI assistant. Plus a work simulation and a work-style survey.
11 questions

Most-reported: AI-Assisted Debugging OA: Format, Strategy and Reported Repos (29 reports)
Mostly object-oriented design (Amazon Locker, pizza ordering, shopping cart and checkout, restaurant waitlist, pub/sub notifications, library management, a deck of cards), and a distributed job scheduler.
8 questions

Most-reported: Design Amazon Locker (OOD) (5 reports)
Coding screens: out-of-order package IDs, arranging package weights, LeetCode-style problems such as two sum, and for frontend roles a React grid of wrapping boxes, with Leadership Principle questions alongside.
3 questions

Most-reported: Arrange Packages So Adjacent Weights Sum Below 10 (2 reports)
For Applied Scientist roles: an ML breadth screen (bias and variance, overfitting, regularisation, A/B testing) and ML system design for search ranking and recommendation.
2 questions

Most-reported: Applied Scientist ML Breadth: Bias-Variance, Overfitting and Fundamentals (10 reports)

Which Amazon interview questions come up most?

These are the Amazonquestions reported most across the loops we’ve indexed, sorted by candidate-report frequency.

QuestionRoundReportedLast seen
Deliver Results: Tight Deadlines and Missed CommitmentsBehavioral44×September 2026
Earn Trust and Have Backbone: Conflict, Disagreement and InfluenceBehavioral41×October 2026
Using Generative AI: How You Use It and When It Gets Things WrongBehavioral38×October 2026
AI-Assisted Debugging OA: Format, Strategy and Reported ReposOA29×October 2026
How Amazon Leadership Principle Rounds Work (and the HM Deep Dive)Behavioral28×September 2026
Ownership: Beyond Your Area of ResponsibilityBehavioral28×October 2026
Work Simulation and Work Style SurveysOA26×October 2026
Dive Deep: Root Causes and Problems That Needed AnalysisBehavioral23×October 2026
Your Hardest Challenge: Obstacles and Complex ProblemsBehavioral21×October 2026
Course Schedule and Dependency Ordering (Topological Sort)Coding16×September 2026

The full index is below, or browse the Amazon catalog →

Every Amazoninterview question we’ve indexed

All 57, grouped by round and sorted by how often candidates reported them. Each links to the question, its reported follow-up count, and when it was last seen.

Coding (20)

Behavioral (13)

OA (11)

System Design (8)

Phone Screen (3)

MLE (2)

What passing candidates do
  • •Preparing a set of STAR stories tagged by Leadership Principle. Candidates report a dozen or more stories across the loop, and are often asked what they would do differently, even about projects that went well.
  • •Having a concrete answer about generative AI: how you use it, how you validate its output, and a time it was wrong and how you noticed.
  • •In the AI debugging task, reproducing from the README and reading the failing tests before changing code. Some candidates who passed only part of the tests still advanced.
  • •Talking through complexity and dry-running the code, which interviewers ask for on the LeetCode-style rounds.
Where candidates lose points
  • •Stories that are not technical enough. In one L5 loop an interviewer asked the candidate to swap in a more technical story.
  • •Fixing only what the UI shows in the debugging task. In the issue-tracker repository the tests check the status code and message the API returns.
  • •Running out of time in the debugging task. One candidate recalls that time left over from the algorithm question does not carry over, so plan for the full 60 minutes.

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Every question. Every candidate-reported follow-up. The mistakes that sink people, and what passers do instead. Monthly refresh.

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Send them this guide: 57 questions from 542 candidate reports, with the rounds, the follow-ups and what passers do.

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