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 achievements, ambiguity, conflict, efficiency, challenges

Describe achievements, ambiguity, conflict, efficiency, challenges Behavioral Technical Screen — Multi-part Prompt Context: You are interviewing for a...

Behavioral & Leadership
2
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
46 people solved
Jul 15, 2025
Amazon logo
Amazon
Medium
Software Engineer

Find first unique character

Find first unique character Given a lowercase/uppercase alphanumeric string s, return the index of the first character that appears exactly once; if n...

Coding & Algorithms
3
0
32 people solved
Jul 15, 2025
Amazon logo
Amazon
Medium
Data Scientist

Handle Missing Values and Choose ML Algorithms Wisely

ML Interview: Core Modeling Concepts You are in a technical phone screen for a Data Scientist role. Assume primarily tabular datasets and address both...

Machine Learning
62
0
198 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Calculate Weekly, Monthly Watch Hours for Paid Users

video_view_logs +---------+----------+----------------+------------+------------+ | user_id | video_id | watched_seconds| watch_date | device_type| +-...

Data Manipulation (SQL/Python)
87
0
212 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Identify First Daily Order for Each Merchant

Orders +----------+-------------+---------+------------+ | order_id | merchant_id | amount | order_date | +----------+-------------+---------+-------...

Data Manipulation (SQL/Python)
76
0
274 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Optimize Email Strategy for New Prime Video Series Launch

Optimizing Email Strategy for a New Prime Video Series Launch You are designing, deploying, and evaluating ranking models and marketing emails for Pri...

Machine Learning
69
0
30 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Evaluate Cost, Time, Security, Flexibility for Training Options

Evaluate Cost, Time, Security, and Flexibility for Training Options Your company needs to roll out an organization-wide employee training program. The...

Behavioral & Leadership
91
0
321 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Explore Subscription Patterns and Status Transitions with SQL/Pandas

subscriptions +-----------------+---------+-------------+ | subscription_id | status | status_date | +-----------------+---------+-------------+ | 10...

Data Manipulation (SQL/Python)
67
0
8 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

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...

Machine Learning
14
0
62 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Optimize Feature Selection and Handling in Machine Learning Models

Optimize Feature Selection and Handling in Machine Learning Models You are building a customer propensity model to predict whether a user will purchas...

Machine Learning
20
0
116 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Data Scientist

Demonstrate Initiative Beyond Job Responsibilities

Demonstrate Initiative Beyond Job Responsibilities This behavioral prompt evaluates ownership, curiosity, and willingness to solve ambiguous technical...

Behavioral & Leadership
16
0
49 people solved
Jul 12, 2025
Amazon logo
Amazon
Hard
Data Scientist

Build Accurate Energy Consumption Prediction Model for Utilities

Build an Energy Consumption Prediction Model for Utilities You need to build and productionize a regression model that predicts daily energy consumpti...

Machine Learning
15
0
51 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Identify Most Popular First-Watched Movie in Viewing History

MOVIE_VIEWS +------------+--------------------+------------+ | customer_id| title | date | +------------+--------------------+-----...

Data Manipulation (SQL/Python)
5
0
18 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Identify Unique Unordered City Pairs in Flight Log

FLIGHTS +----------------+---------------+ | departure_city | arrival_city | +----------------+---------------+ | NYC | LAX | | ...

Data Manipulation (SQL/Python)
3
0
3 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Analyze Seller Compliance and Customer Purchase Patterns

SELLER_STATUS +-----------+------------+-----------+ | seller_id | date | status | +-----------+------------+-----------+ | 1 |2019-0...

Data Manipulation (SQL/Python)
5
0
19 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Illustrate SQL Join Results with Duplicate Keys

TABLE1 +------+ | col1 | +------+ | 1 | | 1 | | 1 | +------+ ​ TABLE2 +------+ | col1 | +------+ | 1 | | 1 | | 1 | | 1 | | 1 |...

Data Manipulation (SQL/Python)
8
0
18 people solved
Jul 12, 2025
Amazon logo
Amazon
Hard
Product Manager

Launching Alexa in a New-Language Market

Product Case: Launch Alexa in a New Language Market You are the Product Manager for Alexa and must launch Alexa in a country where the primary languag...

Product / Decision Making
8
0
36 people solved
Jul 4, 2025
Amazon logo
Amazon
Medium
Product Manager

Cloud Connection Issue Triage

Incident Triage Prompt: Customer-Reported Cloud Connection Issue You are the on-call Product Manager partnering with Support, SRE, and Engineering dur...

Product / Decision Making
18
0
44 people solved
Jul 4, 2025
Amazon logo
Amazon
Medium
Product Manager

Answer AWS PM behavioral prompts

You are preparing for a Product Manager behavioral interview. Build structured, interview-ready answers for this prompt cluster from the original inte...

Behavioral & Leadership
3
0
46 people solved
Jan 19, 2024

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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