Amazon Interview Questions
Practice 631 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.
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How would you evaluate adding video ads?
You are a data scientist for a free-to-play mobile game. The product team wants to introduce video ads (e.g., rewarded videos and/or interstitial vide...
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. ...
Explain parallelism and collectives in training
Parallelism strategies and communication in large-scale training You are designing a distributed training setup for very large neural networks that ca...
Design a file search module like UNIX find
Design Task: Object-Oriented module that mimics UNIX find Context Design an object-oriented library that replicates the core functionality of the UNIX...
List hyperparameter tuning methods
Describe common methods for hyperparameter tuning in machine learning. For each method, explain: - How it works conceptually. - Its advantages and dis...
Explain surprisal and its units
You are discussing a language-modeling / NLP project. The interviewer asks about surprisal. 1. Define surprisal for an event/token with probability \(...
Explore Dataset to Assess Quality and Choose Visualizations
Understanding a New Dataset: Profiling, Quality, and Visualization You receive a new, unfamiliar dataset and must quickly generate insights and visual...
Explain random forests, bagging, and evaluation
Random Forests, Bagging vs Boosting, and Practical Model Validation You are building a supervised learning model on tabular data. Explain and compare ...
Write and explain gradient descent pseudocode
Task: Batch Gradient Descent for Linear Regression (with Intercept) You are interviewing for a Data Scientist role and are asked to implement batch gr...
Evaluate concession gift-card policy with DID
Evaluate a Gift-Card Concession Pilot (Causal Impact with Staggered Adoption) Context Several regions piloted a policy: when a shipment is lost or dam...
Design a cloud storage service
Design a cloud storage service System Design: Cloud Document Storage and Sharing Service Context Design the backend for a large-scale cloud document s...
Compute unique visitors per department from clicks
Given tables Products(product_id, department, category, subcategory) where department > category > subcategory form a hierarchy, and ClickLog(user_id,...
Amazon Leadership Principles – Behavioral Deep Dive
Amazon Leadership Principles Behavioral Deep Dive Prepare for an Amazon Product Manager Leadership Principles behavioral interview. For each prompt, t...
Resolve Conflicts Between Data Findings and Team Opinions
Resolve Conflicts Between Data Findings and Team Opinions Behavioral Scenario: Resolving Conflicts Between Data Findings and Team Beliefs Scenario You...
Determine Discount's Effect on Conversion Rate with A/B Testing
Determine Discount's Effect on Conversion Rate with A/B Testing A/B Test Design: 10% Discount Impact on Conversion Scenario An e-commerce retailer wan...
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...
Compare Regularization Techniques and Their Use Cases
Compare Regularization Techniques and Their Use Cases This technical phone screen asks about model evaluation, regularization, and regression basics f...
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...
Handle cold start, dropout, and training stability
Machine Learning deep dive Answer the following conceptual questions (you may use equations and small examples). A) Recommender systems: cold start 1....
Explain ML evaluation, sequence models, and optimizers
Scenario An interviewer is deep-diving into an ML project you built (you can assume it is a supervised model unless specified otherwise). They want yo...