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Amazon interview questions

653 practice questions in the style of Amazon's interviews, covering Behavioral & Leadership, System Design, Coding & Algorithms, Product / Decision Making, Data Manipulation (SQL/Python), Analytics & Experimentation, Statistics & Math, Software Engineering Fundamentals, Machine Learning, ML System Design, Product Design & Strategy.

Questions653

50 easy471 medium132 hard

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Amazon interview questions by the numbers

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Katkat has 653 Amazon interview questions. The largest topics are Coding & Algorithms (35%), Behavioral & Leadership (26%) and System Design (9%). By difficulty: 8% easy, 72% medium and 20% hard. The most common round is Technical Screen (49%). Frequent concepts include System Design, Behavioral, Leadership and Algorithms.

Difficulty

  • Easy8% · 50
  • Medium72% · 471
  • Hard20% · 132

Interview rounds

  • Technical Screen49% · 318
  • Onsite37% · 243
  • Online Assessment11% · 71
  • Other2% · 13
  • HR Screen1% · 8

Roles

  • Software Engineer61% · 400
  • Data Scientist22% · 144
  • Applied Scientist8% · 54
  • Product Manager3% · 22
  • Business Intelligence Engineer2% · 13

Frequent concepts

  • System Design5% · 34
  • Behavioral5% · 32
  • Leadership4% · 29
  • Algorithms4% · 23
  • Distributed Systems4% · 23
  • Optimization3% · 21

Showing 31–60 of 653 results

  • Evaluates culture-fit behavioral answers for inventing and simplifying, customer communication, and innovation. Strong answers use STAR, show stakeholder judgment, quantify impact, and reflect on lessons.

    AmazonData ScientistBehavioral & LeadershipOnsite
  • This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess cooldown and plan interview strategy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerBehavioral & LeadershipOnsite
  • Practice behavioral deep-dive and leadership scenarios for Product Managers, including high-impact projects, cross-functional programs, complex launches, process improvement, strategy focus, influence, disagreement, limited-data decisions, leadership principles, and input metrics.

    AmazonProduct ManagerBehavioral & LeadershipTechnical Screen
  • Practice Amazon Product Manager behavioral scenarios with structured STAR examples for data-driven problem solving, complex constraints, difficult customers, fast decisions, and ownership beyond scope. The solution emphasizes metrics, trade-offs, guardrails, reusable mechanisms, and interview-ready answer patterns.

    AmazonProduct ManagerBehavioral & LeadershipOnsite
  • Practice behavioral PM answers about delivering results amid ambiguity and resolving principled disagreement. The guide uses STAR examples to show customer problem framing, metrics, experiments, decision docs, stakeholder alignment, respectful conflict, disagree-and-commit behavior, and measurable outcomes.

    AmazonProduct ManagerBehavioral & LeadershipTechnical Screen
  • A two-part behavioral question from a software engineering onsite: describe a conflict with a coworker and how it was resolved, then explain how you have used generative AI in your work. It tests ownership, settling disagreements with evidence, verifying AI output, and judgment about the limits of AI tools.

    AmazonSoftware EngineerBehavioral & LeadershipOnsite
  • Brainstorm a business problem approach evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerAnalytics & ExperimentationTechnical Screen
  • This problem tests the ability to build a robust, accessible UI component that manages asynchronous data fetching, state synchronization, and DOM-level focus management.

    AmazonFrontend EngineerSoftware Engineering FundamentalsOnsite
  • This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.

    AmazonData ScientistData Manipulation (SQL/Python)HR Screen
  • This question evaluates proficiency in SQL-based data manipulation and panel construction for staggered-adoption difference-in-differences, testing skills in handling adoption dates, event-time calculations, boundary conditions, and aggregation in the Data Manipulation (SQL/Python) domain.

    AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
  • This question evaluates understanding of breadth-first traversal, URL normalization, domain scoping, duplicate detection, and retry/error handling, measuring algorithmic design and robustness in networked code.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to design an in-memory inverted index for text retrieval, requiring efficient tokenization, case-insensitive mapping, and dynamic updates to a collection of sentences.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This question assesses a candidate's ability to think beyond immediate team requirements, identifying systemic inefficiencies and designing scalable solutions that provide cross-functional value.

    AmazonSoftware EngineerBehavioral & LeadershipOnsite
  • This question evaluates a candidate's ability to design an end-to-end ML-driven package-to-courier allocation system, testing competencies in predictive modeling (per-stop service-time forecasting), constrained combinatorial optimization for routing and fairness, feature engineering, simulation, and production concerns such as cold-start handling and distribution shift. It is asked to assess how candidates balance competing objectives like on-time delivery versus fairness under real-world constraints (shift hours, vehicle capacity, delivery windows, time-varying travel forecasts), and is categorized under Machine Learning with a focus on practical application-level system design rather than purely conceptual theory.

    AmazonData ScientistMachine LearningOnsite
  • This problem tests your proficiency in creating responsive UI layouts using CSS Flexbox or Grid and your ability to optimize rendering performance for large datasets using virtualization or efficient list rendering.

    AmazonSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • This problem tests your ability to design a cache with O(1) time complexity for both get and put operations by combining a hash map with a doubly linked list.

    AmazonSoftware EngineerCoding & AlgorithmsHR Screen
  • Build an end-to-end ML pipeline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonApplied ScientistML System DesignOnsite
  • This problem tests your ability to use a greedy approach combined with a frequency map to construct the lexicographically largest sequence under specific constraints.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question tests string manipulation, modular arithmetic, and the ability to optimize performance by trading space for time using a precomputed lookup table.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates programming fluency in numerical data processing and algorithmic implementation for computing moving averages and handling edge cases in time-series sequences, and it applies to Data Scientist roles while falling under the Coding & Algorithms category.

    AmazonData ScientistCoding & AlgorithmsOnsite
  • This question evaluates skills in experimental design and statistical inference, including sample size calculation, hypothesis testing, confidence interval interpretation, multiple comparison control, variance reduction techniques, and operational considerations for A/B testing.

    AmazonData ScientistStatistics & MathOnsite
  • This question evaluates a candidate's ability to reason about circular arrays, distance computations, and query-efficient algorithms, assessing concepts such as modular indexing and cumulative distances.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competency in SQL-based data manipulation for a data scientist role, covering joins, aggregations, date-based filtering, metric computation, and use of window functions to derive week-over-week changes.

    AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
  • This question evaluates proficiency in SQL-based data manipulation, including aggregation, date extraction, and combining transactional tables to produce business metrics, and falls under the Data Manipulation (SQL/Python) domain.

    AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
  • This question evaluates proficiency in time-series aggregation and group-wise metric computation on structured data, specifically calculating rolling seven-day sums of answers per device, and assesses familiarity with data manipulation using SQL or Python.

    AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
  • This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.

    AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
  • An Amazon leadership-principles behavioral question in two parts: a calculated risk you took when speed was critical, and an important project you delivered under a tight deadline. It tests bias for action, risk judgment, scope negotiation and delivering results at a senior level.

    AmazonSoftware EngineerBehavioral & LeadershipOnsite
  • Tests string manipulation and two-pointer traversal techniques to verify if a string is a palindrome while ignoring non-alphanumeric characters and case.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of graph algorithms, reachability, and algorithmic complexity by determining whether adding a directed edge would introduce a cycle in an otherwise acyclic directed graph.

    AmazonApplied ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of tree traversal and sequence matching in N-ary trees, including handling non-unique node values and maintaining traversal state across branches.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite

Amazon interview experiences

All 396 →
  • L6 Phone Screen With an Impatient Interviewer, LCA Follow-up StuckAmazon · Software Engineer · Senior+ · Technical Screen · Oct 2026

    The interviewer was impossible to find anywhere online, I couldn't find a thing about him and I'm speechless. BQ: 1. How do you evaluate customer experience? 2. A major unexpected obstacle. For the first one, I think the point of the question was the evaluation part, and my story was just directly collecting feedback, which he wasn't happy with 😭 For the second one, he felt the obstacle wasn't ma…

  • Three Rounds of Graphs, System Design and Leadership PrinciplesAmazon · Software Engineer · Technical Screen · Oct 2026

    I interviewed with the Agentic AI security product team under AWS. The location is Austin. Every round was 1 hour. Round 1 was a DAG topological sort. It was pretty good, pretty basic. Round 2 was another directed graph problem. This team really likes graph problems. It was something like working out what you need to make a cake, and it is very similar to a LeetCode problem. Round 3 was a traditi…

  • Four Rounds with Detailed Behavioral QuestionsAmazon · Software Engineer · Onsite · Sep 2026

    The author describes four Amazon virtual-onsite interviews for an SDE2 role, each combining behavioral discussion with technical work. The technical topics were dependency ordering and selective reruns, a high-volume inventory-update service, an extensible document-search library, and merging timestamp-ordered event streams without loading everything into memory. Behavioral questions examined inv…

  • One DSA Round, Two Mediums, RejectedAmazon · Frontend Engineer · Technical Screen · Sep 2026
    Rejected

    I applied through a LinkedIn post for SDE I (Frontend). HR contacted me over a call and we discussed the further rounds. Round 1 (PS-DS Round) (1hr) Two DSA medium questions were asked, and I was able to solve both of them within the time limit (~40 mins). The interviewer was helpful and provided hints. Q1. Lowest Common Ancestor of a Binary Search Tree Q2. Ones and Zeroes Explain brute force, th…

  • Three BQ questions: Q1: Can you tell me about a time when you pushed back against a decision that negatively impacted your team? What was the issue? How did it turn out? Q2: Can you tell me about a time when a system was failing intermittently, and the very obvious metrics were all looking good? How did you find the root cause, and how long did it take? Q3: Can you tell me about a problem you had…

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