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
Medium
Machine Learning EngineerSenior+

Explain XGBoost Parallelism Strategies

Explain How XGBoost Parallelizes Training Scope Describe how XGBoost achieves parallelism: 1. Within a single machine - Histogram-based split findi...

Machine Learning
7
0
77 people solved
Sep 6, 2025
Amazon logo
Amazon
Hard
Software Engineer

Design a log filtering and analytics service

Question Design a log-processing service that ingests application logs at scale and supports the following capabilities: 1. Filter logs by attributes ...

System Design
14
0
117 people solved
Sep 6, 2025
Amazon logo
Amazon
Hard
Machine Learning Engineer

Explain imbalance, metrics, bias-variance, Transformers vs. CNNs

Question You are given a highly imbalanced binary classification problem in a fraud-detection setting (roughly 1% positives). Walk through the core ML...

Machine Learning
9
0
61 people solved
Sep 6, 2025
Amazon logo
Amazon
Medium
Software Engineer

Design an OOD restaurant management system

Object-oriented design: Restaurant management system Design an object-oriented system for a dine-in restaurant that supports the following: Functional...

Software Engineering Fundamentals
10
0
96 people solved
Sep 5, 2025
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Amazon
Medium
Software Engineer

Design a GitHub-like user community

System design: GitHub-like user community Design the backend for a “developer community” product inspired by GitHub’s social layer. Core requirements ...

System Design
1
0
37 people solved
Sep 5, 2025
Amazon logo
Amazon
Medium
Software Engineer

Design object model for an elevator system

Design the object-oriented model for an elevator (lift) system in a multi-floor building that may have multiple elevators. The system should support: ...

Software Engineering Fundamentals
10
0
80 people solved
Oct 31, 2025
Amazon logo
Amazon
Medium
Software Engineer Locked

Design a scalable image-based social network

This question evaluates a candidate's ability to design large-scale backend architectures, covering distributed systems, storage and media handling, c...

System Design
6
0
57 people solved
Oct 31, 2025
Amazon logo
Amazon
Hard
Software Engineer Locked

Minimize time for two handlers

This question evaluates algorithmic optimization skills and stateful scheduling reasoning, focusing on task assignment trade-offs and cumulative cost ...

Coding & Algorithms
7
0
99 people solved
Apr 7, 2026
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Amazon
Medium
Software EngineerIntern Locked

Maximize capacity with primary-backup pairing

This question evaluates combinatorial optimization and constrained matching skills, focusing on algorithm design, complexity analysis, and resource-al...

Coding & Algorithms
5
0
49 people solved
Jan 6, 2026
Amazon logo
Amazon
Medium
Software Engineer Locked

Design a large-scale temperature sensor system

This question evaluates system design and distributed-systems competencies including large-scale data ingestion, time-series storage, real-time visual...

System Design
9
0
89 people solved
Jan 6, 2026
Amazon logo
Amazon
Medium
Software Engineer Locked

Design a package installer with dependencies

This question evaluates understanding of package management and dependency resolution, including version constraints, detection of missing packages, c...

System Design
7
0
60 people solved
Jan 1, 2026
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Amazon
Hard
Software Engineer

Design feedback and feature rollout platform

Design feedback and feature rollout platform System Design: Feedback + Feature Rollout Platform Context Build a multi-tenant platform that lets produc...

System Design
4
0
50 people solved
Aug 8, 2025
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Amazon
Medium
Software Engineer

Answer Amazon Leadership Principles questions

Answer Amazon Leadership Principles questions Behavioral Interview Prep: Amazon Leadership Principles (Software Engineer, Onsite) Context You are prep...

Behavioral & Leadership
13
0
93 people solved
Aug 7, 2025
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Amazon
Medium
Data Scientist

Compute CIs, power, and multiple testing

A/B Testing Stats: Confidence Intervals, Power, Multiple Testing, and Clustering Context: You are planning an A/B experiment on a Bernoulli outcome (c...

Statistics & Math
7
0
67 people solved
Oct 13, 2025
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Amazon
Hard
Data Scientist

Compare Tableau live vs extract and filters

Scenario You are building an interactive dashboard over a 100M-row fact table. Compare Tableau connection options and performance behaviors for this s...

Analytics & Experimentation
6
0
76 people solved
Oct 13, 2025
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Amazon
Hard
Data Scientist Locked

Explain random forests, bagging, and evaluation

This question evaluates understanding of ensemble learning and model evaluation, covering Random Forest aggregation, feature subsampling, bagging vers...

Machine Learning
5
0
54 people solved
Oct 13, 2025
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Amazon
Hard
Data Scientist

Analyze an A/B test over last 7 days

A/B Test Readout and Decision (2025-08-26 to 2025-09-01) Context A 50/50 A/B experiment on the checkout flow ran for 7 days, from 2025-08-26 through 2...

Analytics & Experimentation
5
0
69 people solved
Oct 13, 2025
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Amazon
Hard
Data Scientist Locked

Plan and analyze an A/B test

This question evaluates expertise in experimental design and applied statistics — specifically power and sample-size calculations, clustering and desi...

Statistics & Math
5
0
66 people solved
Oct 13, 2025
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Amazon
Hard
Data Scientist

Drive stakeholder alignment under trade-offs

Decision Framework: Training Platform (Standard Vendor vs. Premium Vendor vs. Internal Build) Context You are responsible for driving a cross-function...

Behavioral & Leadership
3
0
32 people solved
Oct 13, 2025
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Amazon
Medium
Data Scientist

Compute p-values, CIs, and adjust multiples

Hypothesis testing and intervals in practice. Part A (z vs t): You sample n = 15 observations from a population with unknown variance and observe samp...

Statistics & Math
5
0
75 people solved
Oct 13, 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.

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