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
Software Engineer

Describe projects and handle challenges

Describe projects and handle challenges Behavioral Interview Prompts — Software Engineer (Onsite) Context: In an onsite behavioral interview for a Sof...

Behavioral & Leadership
8
0
76 people solved
Aug 9, 2025
Amazon logo
Amazon
Hard
Software Engineer

Design an e-commerce recommendation system

Design an Amazon-Scale E‑Commerce Product Recommendation System Context You are designing a large-scale recommendation system that powers multiple use...

ML System Design
14
0
141 people solved
Sep 6, 2025
Amazon logo
Amazon
Hard
Machine Learning EngineerSenior+

Design a Multimodal Neural Network

Design Prompt: Multimodal Text–Image Retrieval and Classification Context You are building a production system that uses both text (titles/description...

ML System Design
11
0
115 people solved
Sep 6, 2025
Amazon logo
Amazon
Hard
Software Engineer

Design secure multi-tier cloud infrastructure

System Design: Multi-tier VPC Architecture for a Large-Scale Application You are designing and deploying a production-ready, multi-tier network on AWS...

System Design
7
0
73 people solved
Sep 6, 2025
Amazon logo
Amazon
Easy
Data Scientist Locked

How to evaluate adding video ads in a game

This question evaluates skills in product analytics, experimentation design, causal inference and monetization modeling for free-to-play mobile games,...

Analytics & Experimentation
4
0
79 people solved
Dec 9, 2025
Amazon logo
Amazon
Medium
Machine Learning Engineer

Design an LLM quality validation system

You are asked to design an end-to-end LLM quality validation system for a team that trains and serves large language models. The goal is to automatica...

ML System Design
6
0
107 people solved
Dec 8, 2025
Amazon logo
Amazon
Medium
Product Manager

Amazon PM Behavioral & Leadership Deep-Dive

Amazon Product Manager Behavioral and Leadership Deep-Dive The Amazon Product Manager onsite loop is a behavioral deep-dive. Across several interviewe...

Behavioral & Leadership
34
0
214 people solved
Jul 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

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...

Analytics & Experimentation
6
0
71 people solved
Nov 4, 2025
Amazon logo
Amazon
Hard
Software Engineer

Design ad clickstream analytics pipeline

Design ad clickstream analytics pipeline Design an end-to-end advertising clickstream ingestion and analytics platform that ingests events through Kaf...

System Design
12
0
89 people solved
Jul 31, 2025
Amazon logo
Amazon
Hard
Software Engineer

Evaluate actions in Amazon simulation

Evaluate actions in Amazon simulation Amazon Work Simulation: Purpose, Modules, and Design Decisions Context and Assumptions The Work Simulation is a ...

Behavioral & Leadership
114
1
512 people solved
Jul 29, 2025
Amazon logo
Amazon
Medium
Software Engineer

Solve two set and graph problems

Implement solutions for the following two data-structure / algorithm problems. --- Problem 1: Friend-purchase recommendations You are given three enti...

Coding & Algorithms
18
0
166 people solved
Oct 31, 2025
Amazon logo
Amazon
Medium
Software Engineer AI

Calculate Circular Route Query Distance

You are given a circular route with n stops numbered 0 to n - 1. The array distances has length n, where distances[i] is the clockwise distance from s...

Coding & Algorithms
0
0
10 people solved
Apr 6, 2026
Amazon logo
Amazon
Medium
Software EngineerSenior+ Locked

Implement Event Filtering and Queue Routing

This question evaluates parsing and evaluation of domain-specific filter expressions, JSON traversal and type-aware comparisons, boolean logic handlin...

Coding & Algorithms
2
0
16 people solved
May 30, 2026
Amazon logo
Amazon
Hard
Software EngineerSenior+

Evaluate IP Access Rules

You are given a list of IPv4 access-control rules. Each rule consists of: - an action: allow or deny - a CIDR block such as 192.168.0.0/16 You are als...

Coding & Algorithms
15
0
205 people solved
Feb 17, 2026
Amazon logo
Amazon
Medium
Machine Learning Engineer Locked

Debug online worse than offline model performance

This question evaluates the ability to diagnose discrepancies between offline and online model performance by reasoning about data distributions, feat...

ML System Design
5
0
63 people solved
Jan 6, 2026
Amazon logo
Amazon
Medium
Software EngineerSenior+ Locked

Explain Why You Are Considering a Job Change

Shape a concise, truthful answer to why you are considering a job change. Connect a durable motivation and one concrete fact from your current experie...

Behavioral & Leadership
0
0
8 people solved
May 29, 2026
Amazon logo
Amazon
Medium
Software Engineer Locked

Schedule vector ops on heterogeneous cores

This question evaluates understanding of scheduling and load-balancing heuristics for heterogeneous processors, algorithmic complexity reasoning, and ...

Coding & Algorithms
8
0
75 people solved
Jan 2, 2026
Amazon logo
Amazon
Easy
Data Scientist

Solve two string DP/hash problems

Solve the following two coding questions. 1) Unique Morse Code Transformations You are given an array of strings words (lowercase English letters). Us...

Coding & Algorithms
79
0
599 people solved
Feb 13, 2026
Amazon logo
Amazon
Medium
Software Engineer Locked

Explain overfitting, regularization, and LLM techniques

This question evaluates understanding of model generalization (overfitting vs underfitting), regularization methods (L1 vs L2), modern LLM techniques ...

Machine Learning
8
0
129 people solved
Feb 12, 2026
Amazon logo
Amazon
Medium
Data Scientist

Build DID panel and compute effects in SQL

Using the schema and toy data below, write SQL to construct a user-week panel and compute a clean pre/post DID dataset for first reminder exposure. Re...

Data Manipulation (SQL/Python)
6
0
108 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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