Amazon Interview Questions

Amazon Coding & Algorithms Interview Questions

Practice 689 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.

689 Questions 1 Company08.11.2026
Showing 20 results
Role
Amazon logo
Amazon
Medium
Data Scientist

Describe Solving Complex Project Challenges in Detail

Describe Solving Complex Project Challenges in Detail Behavioral: Ownership and Problem-Solving (Data Scientist Phone Screen) Prompt Describe the most...

Behavioral & Leadership
2
0
30 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Ensure Correct Numeric Ordering in Visit ID Comparison

visits +----------+---------+-----------+---------------------+ | visit_id | user_id | page | visit_ts | +----------+---------+-------...

Data Manipulation (SQL/Python)
0
0
6 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Deliver a Data Solution Under Tight Deadlines

Deliver a Data Solution Under Tight Deadlines Behavioral Prompt: Delivering Under a Tight Timeline Scenario A critical product launch date was moved u...

Behavioral & Leadership
47
0
177 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Select Top Customers Using Transaction Data Filters

transactions +----+---------+------------+--------+ | id | user_id | order_date | amount | +----+---------+------------+--------+ | 1 | 101 | 202...

Data Manipulation (SQL/Python)
249
1
747 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Retrieve First Active and Last Inactive Dates per User

Given a table activity that tracks user activities, write a SQL query to retrieve the first active date and last inactive date for each user. Table Sc...

Data Manipulation (SQL/Python)
187
9
357 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOIN

employees +----+--------+---------+ | id | name | dept_id | +----+--------+---------+ | 1 | Alice | 10 | | 2 | Bob | 20 | | 3 | Car...

Data Manipulation (SQL/Python)
77
0
140 people solved
Aug 4, 2025
Amazon logo
Amazon
Hard
Data Scientist

Demonstrate Leadership in Challenging Situations and Decision-Making

Demonstrate Leadership in Challenging Situations and Decision-Making Amazon Data Scientist Onsite — Bar-Raiser Behavioral Loop Context You will be ask...

Behavioral & Leadership
81
0
253 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Data Scientist

Diagnose Business Decline Using Key Data Metrics

Diagnose Business Decline Using Key Data Metrics Diagnose a Sudden Business Downturn Using Data Context You are the data scientist supporting a large ...

Analytics & Experimentation
6
0
37 people solved
Aug 4, 2025
Amazon logo
Amazon
Medium
Software Engineer

Demonstrate leadership with STAR examples

Demonstrate leadership with STAR examples Behavioral (STAR) Prompts for a Software Engineer Onsite Context You are preparing for a Software Engineer o...

Behavioral & Leadership
6
0
44 people solved
Jul 31, 2025
Amazon logo
Amazon
Medium
Software Engineer

Solve LeetCode string and list problems

Question LeetCode 767. Reorganize String LeetCode 23. Merge k Sorted Lists LeetCode 138. Copy List with Random Pointer https://leetcode.com/problems/r...

Coding & Algorithms
12
0
45 people solved
Jul 29, 2025
Amazon logo
Amazon
Medium
Software Engineer

Discuss deadline, challenge, feature

Discuss deadline, challenge, feature Behavioral Questions for a Software Engineer Phone Screen These prompts assess your ownership, delivery under pre...

Behavioral & Leadership
8
0
29 people solved
Jul 29, 2025
Amazon logo
Amazon
Medium
Software Engineer

Compute minimum passes over permutation

Question You are given an array shelf representing a permutation of the integers 1..n. Starting with target = 1, you repeatedly scan the array left-to...

Coding & Algorithms
66
0
139 people solved
Jul 29, 2025
Amazon logo
Amazon
Medium
Product Manager

Describe impact, ambiguity, and conflict

You are speaking with an Amazon hiring manager for a non-technical role. Prepare structured answers to the following behavioral prompts: Constraints &...

Behavioral & Leadership
6
0
53 people solved
May 7, 2024
Amazon logo
Amazon
Medium
Machine Learning Engineer

Implement inventory allocation with backorders

Implement inventory allocation with backorders Design and implement a function to process an event stream for an e-commerce marketplace. Input: ( 1) i...

Coding & Algorithms
1
0
28 people solved
Jul 17, 2025
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
Hard
Software Engineer

Explain attention and Transformers

Explain attention and Transformers Scaled Dot-Product Self-Attention, Transformer Architecture, and BERT vs GPT You are interviewing for a software en...

Machine Learning
7
0
69 people solved
Jul 15, 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

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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