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
Data Scientist

Decide standardization, sparse numerics, correlated features

You are given a tabular dataset for supervised learning with features: F1 (counts, mostly small integers with many zeros), F2 (monetary amounts in dol...

Machine Learning
4
0
43 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Design an end-to-end spam detection system

Design an End-to-End Email Spam Detection System You are asked to design a production-grade email spam detection system that meets the following const...

Machine Learning
15
0
131 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Estimate live sports impact on subscriptions

Estimate the Causal Impact of Live Sports on Prime Subscriptions and Engagement Context Amazon is considering adding live broadcasts of selected sport...

Analytics & Experimentation
3
0
48 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Apply Double ML with text-address features

Estimate the ATE of a First Reminder on CSAT via Double Machine Learning (DML) Context You have observational data on customer satisfaction (CSAT) sur...

Machine Learning
7
0
58 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Design causal study for reminder impact

Observational Causal Study: Reminder Program With Staggered Market × Channel Launch Context You are evaluating the causal impact of medication-subscri...

Analytics & Experimentation
6
0
57 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data Scientist

Prioritize Tasks and Respond to Coworker Concerns

Prioritize Tasks and Respond to Coworker Concerns Task Prioritization and Coworker Response Simulation (Data Scientist) Context You are a Data Scienti...

Behavioral & Leadership
8
0
83 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Explain Central Limit Theorem and Its Limitations

Explain Central Limit Theorem and Its Limitations Statistics Concepts and Disease-Test Evaluation Context You are assessing core statistical concepts ...

Statistics & Math
7
0
61 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Describe Your Most Challenging Project and Its Outcome

Describe Your Most Challenging Project and Its Outcome Tell me about the most challenging project, situation, or thing you have worked on as a data sc...

Behavioral & Leadership
24
0
86 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Assess Leadership Through Disagreement, Failure, and Risk Examples

Assess Leadership Through Disagreement, Failure, and Risk Examples Behavioral Leadership Deep-Dive (Data Scientist Onsite) Scenario A leadership-princ...

Behavioral & Leadership
44
0
176 people solved
Aug 4, 2025
Amazon logo
Amazon
Hard
Data Scientist

Design A/B Test for New Amazon Recommendation Module

Design A/B Test for New Amazon Recommendation Module A/B Test Design: Home Page Recommendation Module Scenario Amazon plans to introduce a new product...

Analytics & Experimentation
105
0
329 people solved
Aug 4, 2025
Amazon logo
Amazon
Hard
Data Scientist

Design an ML Model for Interview Recommendation Pipeline

Design an ML Model for Interview Recommendation Pipeline Scenario You are designing and deploying an ML model that mirrors a real-world recommendation...

Machine Learning
71
0
153 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Optimize XGBoost for Predicting Marketing Outcomes

Optimize XGBoost for Predicting Marketing Outcomes Gradient-Boosted Trees for Marketing Outcome Prediction Context You’re building a model to predict ...

Machine Learning
33
0
80 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Software Engineer

Describe diving deep into a problem

Describe diving deep into a problem Behavioral: Dive Deep to Resolve a Complex System Issue Prompt Describe a situation where you had to dive deep int...

Behavioral & Leadership
3
0
46 people solved
Jul 31, 2025
Amazon logo
Amazon
Medium
Software EngineerSenior+

Design a replicated key-value store with quorums

Design a distributed system that replicates key–value pairs across multiple replicas. The interviewer wants to focus on replication, quorums, and fail...

System Design
9
0
87 people solved
Dec 17, 2025
Amazon logo
Amazon
Medium
Machine Learning Engineer

Explain core ML fundamentals

Explain core ML fundamentals ML Fundamentals — Onsite Interview Task Context: Answer the following fundamentals as if in an onsite ML Engineer intervi...

Machine Learning
8
0
71 people solved
Jul 17, 2025
Amazon logo
Amazon
Hard
Machine Learning Engineer

Deep-dive your GenAI project architecture

Deep-dive your GenAI project architecture GenAI System Deep-Dive: End-to-End Design and Scale Strategy Provide a structured walkthrough of a productio...

ML System Design
6
0
76 people solved
Jul 17, 2025
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Amazon
Medium
Software Engineer

Handle deadlines and misses

Handle deadlines and misses This is a two-part behavioral question. Answer both parts: 1. Tight deadline. Describe a time you faced a tight deadline. ...

Behavioral & Leadership
7
0
54 people solved
Jul 15, 2025
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Amazon
Medium
Data Scientist

Determine Probability of Both Children Being Boys

Conditional Probability: Two Children A family has two children. You learn that at least one of them is a boy. Answer the questions below and state yo...

Statistics & Math
22
0
58 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Machine Learning Engineer Locked

Compare float types and design ablation

This question evaluates understanding of floating-point numerical representations and experimental design for ablation studies, testing competencies i...

Machine Learning
2
0
20 people solved
Dec 8, 2025
Amazon logo
Amazon
Easy
Machine Learning Engineer

Contrast CNNs and fully connected networks

Compare convolutional neural networks (CNNs) with fully connected (dense) networks. Explain: - The structural differences between convolutional layers...

Machine Learning
3
0
56 people solved
Dec 8, 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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