Amazon Interview Questions

Amazon Coding & Algorithms Interview Questions

Practice 695 real Amazon interview questions for 2026. Covers all top categories — Coding & Algorithms, Behavioral & Leadership, Machine Learning, Data Manipulation (SQL/Python), and System Design — across Software Engineer, Data Scientist, Machine Learning Engineer, Product Manager, and Business Intelligence Engineer roles. Real Amazon interview questions from actual interviews with detailed solutions; use this collection for interview preparation that emphasizes shipping at scale, measurable impact, and the company’s Leadership Principles. Expect coding-heavy assessments for Software Engineer candidates: frequent tree and dynamic-programming problems, two-array optimization patterns, nested object/path lookups, and system-design prompts that mirror product flows (online Minesweeper, pizza-ordering, credit-card and shipping/cost systems), plus leadership and collaboration behavioral prompts. Data Scientist rounds concentrate on experimentation and metrics (A/B design, hand p-values, D7 retention SQL), RAG/recommender evaluation, and product-impact analyses. ML Engineer questions focus on production model design, LLM/agent concepts, reliability (cold start, training stability, online vs offline gaps), and large-scale detection pipelines. PM interviews stress customer-obsessed stories, ambiguity, Alexa product launches, and domain-specific data pipelines. Prepare with timed coding practice, end-to-end experiment writeups, STAR stories framed to Leadership Principles, and mock system-design sessions.

695 Questions 1 Company08.11.2026
Showing 20 results
Role
Amazon logo
Amazon
Hard
Data Scientist

Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability

Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability Large-Scale Recommendation System: Ensembles, Overfitting, Metrics, Architectu...

Machine Learning
86
0
319 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Identify P-Value Limitations and Complementary Approaches

Identify P-Value Limitations and Complementary Approaches A/B Testing: Limits of P-values and Better Decision Practices Scenario Your team is running ...

Statistics & Math
19
0
46 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Explain Statistical Outputs to Non-Technical Stakeholders

Explain Statistical Outputs to Non-Technical Stakeholders A/B Test Dashboard Interpretation and Core Statistics Concepts Scenario You are reviewing an...

Statistics & Math
84
0
304 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Coordinate Resources and Resolve Conflicts for Project Success

Coordinate Resources and Resolve Conflicts for Project Success Cross-Functional Leadership: Coordination, Conflict Resolution, Influence, and Bottlene...

Behavioral & Leadership
22
0
74 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Diagnose Business Decline Using Key Data Metrics

Diagnose Business Decline Using Key Data Metrics Diagnose a Sudden Business Downturn Using Data Context You are the data scientist supporting a large ...

Analytics & Experimentation
6
0
38 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Software Engineer

Solve string partition and equal-sum package problems

Solve string partition and equal-sum package problems 1) Given a string itemCategories (lowercase English letters) and an integer k (0 ≤ k ≤ 26), coun...

Coding & Algorithms
2
0
44 people solved
Aug 1, 2025
Amazon logo
Amazon
Medium
Software Engineer

Design rolling-window top-K click tracker

Design rolling-window top-K click tracker You receive a high-volume stream of click events (timestamp in ms, url). Implement a data structure with two...

Coding & Algorithms
3
0
43 people solved
Jul 31, 2025
Amazon logo
Amazon
Medium
Software Engineer

Respond to negative feedback and exceed requirements

Respond to negative feedback and exceed requirements Behavioral Question: Negative Feedback and Going Beyond Requirements Context: Software Engineer —...

Behavioral & Leadership
7
0
48 people solved
Jul 31, 2025
Amazon logo
Amazon
Medium
Software Engineer

Reflect on criticism and deadlines

Reflect on criticism and deadlines Behavioral Interview Prompts — Amazon Software Engineer (Technical Phone Screen) Context You are preparing for a te...

Behavioral & Leadership
10
0
36 people solved
Jul 29, 2025
Amazon logo
Amazon
Medium
Software Engineer

Compute minimum passes over permutation

Question You are given an array shelf representing a permutation of the integers 1..n. Starting with target = 1, you repeatedly scan the array left-to...

Coding & Algorithms
66
0
140 people solved
Jul 29, 2025
Amazon logo
Amazon
Medium
Software Engineer

Design a no-repeat top-frequency music player

Design and implement a music player that receives song lists from new users and returns songs to play in frequency order without repeating a song in t...

Coding & Algorithms
4
0
52 people solved
Mar 22, 2025
Amazon logo
Amazon
Medium
Software Engineer

Tell me about exceeding your responsibility

Answer these Amazon-style behavioral interview questions with concrete STAR stories: 1. Tell me about a time you went beyond your formal responsibilit...

Behavioral & Leadership
4
0
53 people solved
Mar 22, 2025
Amazon logo
Amazon
Medium
Data Scientist

Find recommended friend pairs by shared songs

You work on a music app and want to recommend “friend” connections based on listening similarity. Assume the following tables (all timestamps are in U...

Data Manipulation (SQL/Python)
5
0
46 people solved
Dec 20, 2025
Amazon logo
Amazon
Medium
Software Engineer

Solve interval deletion and GCD subarray problems

This Amazon online assessment contains two independent algorithm problems. For each, return the requested value, describe an efficient algorithm, and ...

Coding & Algorithms
6
1
48 people solved
Jul 16, 2025
Amazon logo
Amazon
Medium
Software Engineer

Explain deadlines, going beyond, and persuasion

Explain deadlines, going beyond, and persuasion Behavioral Prompt: Delivery Under Pressure, Exceeding Expectations, and Influencing Stakeholders Conte...

Behavioral & Leadership
4
0
31 people solved
Jul 16, 2025
Amazon logo
Amazon
Medium
Software Engineer

Explain core ML fundamentals

Explain core ML fundamentals Machine Learning Fundamentals: Regularization, Losses, PCA, and Random Forests Assume standard supervised learning with l...

Machine Learning
6
0
47 people solved
Jul 15, 2025
Amazon logo
Amazon
Hard
Software Engineer

Deep dive a resume project

Deep dive a resume project End-to-End ML Project Walkthrough (System Design Focus) Pick one ML project from your experience and walk through it end-to...

ML System Design
5
0
36 people solved
Jul 15, 2025
Amazon logo
Amazon
Medium
Machine Learning EngineerSenior+

Find shortest transformation steps in a word graph

You are given two strings begin and end of the same length, and a list words of distinct strings (also same length). You can transform one string into...

Coding & Algorithms
5
0
74 people solved
Dec 15, 2025
Amazon logo
Amazon
Medium
Machine Learning Engineer

Implement K-means and solve interval/frequency tasks

Task 1 — Describe/implement K-means clustering Given: - A data matrix X with shape (n_samples, d). - An integer k (number of clusters). Explain (or wr...

Coding & Algorithms
7
0
66 people solved
Dec 15, 2025
Amazon logo
Amazon
Medium
Data Scientist

Diagnose Causes and Test Hypotheses for Metric Drop

Diagnosing and Testing a Sudden Metric Drop A large consumer web or mobile product sees its key business metric drop materially and suddenly. Assume s...

Analytics & Experimentation
39
0
130 people solved
Jul 12, 2025

Frequently Asked Questions

How difficult are Amazon interview questions for software, data, and product roles?
Amazon interviews are competitive and deliberately broad: expect medium-to-hard algorithmic coding for software roles, deep design thinking for system and architecture questions, rigorous experiment and metrics work for data roles, and leadership-driven behavioral prompts for product and PM positions. Difficulty scales with level; early-career loops focus on correctness and problem patterns, senior loops demand scalable design, tradeoff justification, and measurable impact. The Bar Raiser raises the bar on long-term ownership and cultural fit. Overall, the process weeds for both technical depth and the ability to explain tradeoffs, so prepare to demonstrate repeatable problem solving under time pressure.
What is the Amazon interview process and where do the top categories and positions appear in the loop?
Amazon typically uses a staged process: resume screen, role-specific assessments or phone screens in some tracks, then the onsite loop of 4–6 interviews including a Bar Raiser. Coding and algorithms rounds dominate for Software Engineer interviews, while system design appears in one or more senior technical rounds. Data Scientist interviews blend SQL/Python casework, A/B test design, and inferential questions. Machine Learning Engineer loops probe model deployment, offline to online debugging, and NLP/agent design. Product and BI roles focus on metrics, product tradeoffs, and Leadership Principles throughout the loop, with role-specific deep dives tied to the job description.
How long should I prepare for Amazon interviews and what should a timeline look like?
Aim for a focused 6–12 week plan for experienced roles and 4–8 weeks for early-career candidates. Weeks 1–3: shore up fundamentals — arrays, trees, graphs, DP, and core SQL windowing. Weeks 4–6: simulate timed coding rounds, practice system design high-level tradeoffs, and build STAR stories mapped to Leadership Principles. Weeks 7–10: run mock loops with behavioral pressure, refine experiment and ML case studies, and rehearse tradeoff conversations. Leave final 1–2 weeks for targeted practice on role-specific themes such as shipping costs, RAG evaluation, or Alexa product scenarios.
Which key subtopics should I master to perform well across Amazon's top roles?
Master algorithmic patterns including trees, dynamic programming, two-pointer and graph traversals, and complexity justification for coding rounds. For system design, focus on APIs, data models, scaling, caching, reliability, and monitoring. Data roles require SQL window functions, cohort and retention analysis, A/B test design and power, and RAG/evaluation methods for ML-backed features. Machine learning engineers must know model stability, cold-start strategies, online-offline mismatch debugging, and agent alignment basics. Product candidates should be fluent in metric design, customer-observed data pipelines, and prioritization with quantifiable impact.
What standout tips and common pitfalls should I know before interviewing at Amazon?
Start every behavioral or technical story with context and measurable outcomes; interviewers care about specific impact and tradeoffs. Use STAR for leadership prompts but emphasize metrics and follow-on changes. In coding, verbalize assumptions, test edge cases, and discuss complexity and alternative approaches. In design rounds, ask clarifying questions, scope deliberately, and justify scaling choices. Common pitfalls include weak quantification of impact, ignoring the Leadership Principles, failing to probe requirements, and delivering designs without operational considerations. Practice mock loops and incorporate Bar Raiser-style feedback to close gaps before the real loop.

Explore more Amazon interview questions

Jump straight to Amazon questions for a specific role or category.

By role
By category
In-depth guides
Across all companies

Featured Amazon interview prep guides

Concept walkthroughs, worked examples, and the real questions from candidate reports.

Editorial prep
Data Scientist
Amazon interview
Read the guide
Editorial prep
Software Engineer
Amazon interview
Read the guide