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
Software EngineerSenior+ Locked

Design multi-tenant ingestion and processing platform

This question evaluates a candidate's ability to architect scalable, multi-tenant data ingestion and processing platforms, assessing competencies in t...

System Design
19
0
166 people solved
Jan 6, 2026
Amazon logo
Amazon
Hard
Machine Learning Engineer Locked

Explain NLP/RL concepts used in LLM agents

This question evaluates proficiency in transformer-based NLP, embedding methods, LLM agent architecture and evaluation, retrieval techniques for RAG, ...

Machine Learning
23
0
214 people solved
Feb 9, 2026
Amazon logo
Amazon
Medium
Software EngineerNew Grad

Answer common leadership prompts

Prepare strong STAR-style answers for the following behavioral prompts from the interview: - Describe an important project you delivered under a tight...

Behavioral & Leadership
6
0
98 people solved
Oct 24, 2025
Amazon logo
Amazon
Medium
Product Manager

Amazon Leadership Principles Behavioral Scenarios

Behavioral STAR Stories: Amazon Leadership Principles Scenarios Prepare concise STAR stories for the following Amazon Product Manager behavioral scena...

Behavioral & Leadership
13
1
129 people solved
Jul 4, 2025
Amazon logo
Amazon
Easy
Software Engineer Locked

Find Unique Target-Sum Pairs

This question evaluates array-processing skills, correctness in handling duplicate values, and the ability to identify unique value pairs whose sums m...

Coding & Algorithms
3
0
26 people solved
May 2, 2026
Amazon logo
Amazon
Easy
Software Engineer Locked

Describe Delivering Under a Tight Deadline

This question evaluates ownership, judgment, prioritization, and the ability to deliver results under time pressure, including how the candidate balan...

Behavioral & Leadership
19
1
293 people solved
May 2, 2026
Amazon logo
Amazon
Hard
Software EngineerSenior+

Discuss Ownership, Learning, Trust, Scope, and Delivery

Discuss Ownership, Learning, Trust, Scope, and Delivery Prepare evidence-based behavioral answers about ownership, learning, thinking broadly, earning...

Behavioral & Leadership
1
0
11 people solved
Jan 1, 2026
Amazon logo
Amazon
Medium
Software Engineer

Find Valid IP Addresses in Files

You are given an absolute or relative path to a directory on the local file system. Write a program that recursively traverses every subdirectory unde...

Coding & Algorithms
37
0
293 people solved
Apr 28, 2026
Amazon logo
Amazon
Easy
Software Engineer

Discuss AI Use, Deadlines, Ambiguity, and Feedback

Discuss AI Use, Deadlines, Ambiguity, and Feedback Prepare evidence-based responses to the following behavioral themes. Use distinct examples where po...

Behavioral & Leadership
1
0
12 people solved
Mar 19, 2026
Amazon logo
Amazon
Medium
Data Scientist

Implement a high-throughput web crawler safely

Design and code (pseudocode acceptable) a multi-threaded web crawler that favors breadth-first discovery while continuously running analysis tasks on ...

Coding & Algorithms
11
0
141 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Software Engineer Locked

Drone Circular Route — Minimum Total Travel Cost

This question tests a candidate's ability to apply dynamic programming to combinatorial optimization, specifically the Traveling Salesman Problem (TSP...

Coding & Algorithms
1
0
8 people solved
Jun 12, 2026
Amazon logo
Amazon
Hard
Software EngineerSenior+

Return the Vertical Traversal of a Binary Tree

Return the Vertical Traversal of a Binary Tree Problem Implement verticalTraversal(nodeValues, left, right, root) -> columns. Node root is at row 0, c...

Coding & Algorithms
0
0
10 people solved
Jan 1, 2026
Amazon logo
Amazon
Medium
Software Engineer

Model an Extensible Employee Cost Calculator

Design an object model and calculation API for the total cost of an employee. Total cost has four components: 1. Base salary from the employee record....

Software Engineering Fundamentals
1
0
13 people solved
Feb 2, 2026
Amazon logo
Amazon
Hard
Software Engineer

Find a Maximum-Sum Window in a Sparse Array

Find a Maximum-Sum Window in a Sparse Array An integer array is represented by constant-value segments instead of individual elements. Each segment [s...

Coding & Algorithms
1
0
14 people solved
Jul 10, 2026
Amazon logo
Amazon
Easy
Data ScientistSenior+

Design and evaluate a RAG system

You are interviewing for an L5 Data Scientist role focused on LLM applications. Design a retrieval-augmented generation (RAG) system for an internal q...

Machine Learning
15
0
119 people solved
Jan 12, 2026
Amazon logo
Amazon
Medium
Data Scientist

How would you test a price increase?

You are a data scientist at a B2C AI video editing software company (subscription-based, with a free trial and paid tiers). Product leadership is cons...

Analytics & Experimentation
5
0
85 people solved
Dec 20, 2025
Amazon logo
Amazon
Hard
Machine Learning Engineer

Describe how you reduced measurable cost

Behavioral question (focus on ownership/delivery): > Tell me about a time you identified and solved a problem that caused measurable cost (e.g., cloud...

Behavioral & Leadership
12
0
102 people solved
Feb 9, 2026
Amazon logo
Amazon
Medium
Software Engineer

Merge K Sorted Arrays Without Duplicates

Implement merge_sorted_unique(sorted_arrays). Each input array is sorted in ascending order. Merge all arrays into one ascending array containing each...

Coding & Algorithms
0
0
2 people solved
Jun 4, 2026
Amazon logo
Amazon
Medium
Software Engineer

Determine Whether All Courses Can Be Completed

Implement can_finish(num_courses, prerequisites). Courses are numbered from 0 through num_courses - 1. Each pair [course, prerequisite] means the prer...

Coding & Algorithms
0
0
2 people solved
Jun 4, 2026
Amazon logo
Amazon
Medium
Software Engineer

Resolve Collisions Between Moving Asteroids

Implement remaining_asteroids(asteroids). Each nonzero integer represents an asteroid moving along one line. Its absolute value is its size; positive ...

Coding & Algorithms
0
0
2 people solved
Jun 4, 2026

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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