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
Hard
Data Scientist

Diagnose and fix underperforming ML model

Rapidly Improving Recall Under Class Imbalance (One-Day Plan) Context You inherit a binary fraud detection model with severe class imbalance (positive...

Machine Learning
8
0
76 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Quantify build-vs-buy training decision

Quantitative Decision Framework for Selecting a New Employee Training Program Context Your company must choose one of three ways to launch a new emplo...

Analytics & Experimentation
2
0
36 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data Scientist

Compute array modes with ties and no-mode rule

Write a function that returns the mode(s) of an integer array. Requirements: if all values are unique, return an empty list (there is no mode); allow ...

Coding & Algorithms
3
0
32 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data Scientist

Optimize precision–recall under class imbalance

You have extreme class imbalance (positive rate ~1%). You score 12 examples as follows (id, true_label, score): A,1,0.92; B,0,0.90; C,0,0.88; D,0,0.70...

Machine Learning
11
0
87 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data ScientistSenior+

Analyze omitted-variable bias in regression

Omitted-Variable Bias, Heteroscedasticity, and Remedies Setup - True data-generating process (DGP): Y = β0 + β1·Temp + β2·Occupancy + ε - Assumption...

Statistics & Math
6
0
52 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data ScientistSenior+

Transform event logs with subscription windows in pandas

Using pandas, compute user-level subscription-aligned revenue and anomalies for September 2025. DataFrames: events(user_id:int, ts:UTC datetime, event...

Data Manipulation (SQL/Python)
0
0
3 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data ScientistSenior+

Design end-to-end regression for energy demand

End-to-End Daily Energy Prediction for Commercial Buildings Context You are asked to design and justify an end-to-end regression system that predicts ...

Machine Learning
5
0
49 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data ScientistSenior+

Demonstrate leadership under disagreement

Behavioral: Disagreeing on a Launch Under Deadline (Data Scientist) You are a Data Scientist interviewing onsite for a behavioral and leadership round...

Behavioral & Leadership
1
0
39 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data Scientist

Solve stock, BFS path, and merge intervals

Solve three coding problems; justify complexity and corner cases. A) Best Time to Buy/Sell Stock (one transaction): Given prices[0..n-1] (integers), r...

Coding & Algorithms
4
0
36 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data Scientist

Verify subscriptions and analyze orders with SQL/Python

You are given two tables. Write SQL and Python (pandas) to answer the sub-questions precisely, handling edge cases, ties, and missing data. Schema - s...

Data Manipulation (SQL/Python)
9
0
66 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data Scientist

Demonstrate problem-solving under resistance

Behavioral: End-to-End Problem Solving with Resistance (STAR) You are interviewing for a Data Scientist role. Provide a STAR-formatted response descri...

Behavioral & Leadership
9
0
77 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Demonstrate ownership and communication under pressure

Behavioral Interview: Ownership, Dive Deep, Raise the Bar (Data Scientist) Provide concise, data-backed stories using STAR (Situation, Task, Action, R...

Behavioral & Leadership
3
0
36 people solved
Oct 13, 2025
Amazon logo
Amazon
Medium
Data ScientistSenior+

Calculate A/B sample size, CI, decision rules

A/B Test Design and Analysis: Signup Funnel You are designing and analyzing a two-arm A/B test for a signup funnel. Assume 1:1 traffic split and indep...

Statistics & Math
10
0
90 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data ScientistSenior+ Locked

Design fraud detection across channels with unknowns

This question evaluates a data scientist's competence in designing and operationalizing multi-channel fraud detection systems, covering cost-sensitive...

Machine Learning
5
0
40 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data ScientistSenior+

Demonstrate leadership in data-driven scenarios

Behavioral & Leadership Prompts for a Data Scientist (Onsite) Instructions: - Answer each prompt with a specific story using the STAR structure (Situa...

Behavioral & Leadership
1
0
32 people solved
Oct 13, 2025
Amazon logo
Amazon
Hard
Data Scientist

Prove and apply statistical ML fundamentals

Technical ML/Statistics Exercises (with precise math and small computations) Assume a standard supervised learning setting with n samples, p features,...

Statistics & Math
7
0
107 people solved
Oct 13, 2025
Amazon logo
Amazon
Easy
Data Scientist

Describe a time you solved a complex problem

Behavioral (Leadership/Ownership): Describe a time when you solved a complex problem by digging into details. In your answer, cover: - The context and...

Behavioral & Leadership
4
0
72 people solved
Oct 11, 2025
Amazon logo
Amazon
Hard
Software Engineer

Maximize total bandwidth of data channel pairs

You are optimizing how information flows through a network of processing nodes. There are n processing nodes. The bandwidth capability of the i-th nod...

Coding & Algorithms
3
0
37 people solved
Oct 1, 2025
Amazon logo
Amazon
Hard
Software Engineer

Maximize memory capacity with primary-backup servers

You are helping to optimize the capacity of a cloud system. There are n servers, where n is always even. The memory capacity of the i-th server is giv...

Coding & Algorithms
3
0
59 people solved
Oct 1, 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
54 people solved
May 7, 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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