Amazon interview questions

Showing 1–30 of 653 results

• A Problem That Needed Deep Analysis: Knowing You Focused on the Right ThingsMedium

  A behavioral question about a problem that required in-depth thought and analysis, how you knew you were focusing on the right things, and what the outcome was. Follow-ups probe precise details of the example, such as an LLM agent's token size, the effect of adding fields, and what completeness meant.

  AmazonSoftware EngineerBehavioral & LeadershipOnsite
• Accept Notification Bursts With Low LatencyMedium

  Design a high-throughput, low-latency notification ingestion and delivery service capable of absorbing sudden 10x traffic spikes while maintaining strict delivery SLOs and downstream rate limits.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Add One Source to Minimize Grid InconvenienceMedium

  Minimize the worst inconvenience in a binary grid by adding one colored source. Movement is allowed in eight directions, and the solution must handle grids that begin with no source at all.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Alexa Domain-Knowledge Data PipelinesHard

  Practice an Alexa knowledge-pipeline system design covering global holidays, animal facts, source reliability, ingestion, calendar normalization, knowledge graphs, answer serving, entity linking, confidence thresholds, misrouted queries, and debugging.

  AmazonProduct ManagerProduct / Decision MakingOnsite
• Amazon Behavioral Deep-DiveMedium

  Prepare for the Amazon PM behavioral deep-dive with a reusable STAR story bank mapped to Leadership Principles. The guide organizes prompts across fit, data, innovation, conflict, trust, coaching, failure, crisis, integrity, and time management, with metrics and probe-ready follow-up details.

  AmazonProduct ManagerBehavioral & LeadershipOther
• Amazon Fit, Motivation & Leadership PrinciplesMedium

  Practice Amazon Product Manager fit and behavioral questions covering why Amazon, why this PM role, changing direction mid-project, fast decisions with limited information, and five-year career goals. The solution emphasizes customer obsession, metrics, guardrails, ownership, and leadership principles.

  AmazonProduct ManagerBehavioral & LeadershipOnsite
• Amazon Leadership Principles Behavioral ScenariosMedium

  A consolidated bank of 24 Amazon product manager onsite behavioral questions, covering dive deep and root-cause analysis, scaling, influence and disagreement, customer obsession, ownership, mistakes and missed commitments, decisions under ambiguity, Think Big, and one cloud-connectivity triage hypothetical. Each prompt comes with a worked STAR outline, quantified results, and the Leadership Principles the interviewer is scoring. Includes a story-bank map that collapses all 24 prompts into 10 to 12 reusable stories.

  AmazonProduct ManagerBehavioral & LeadershipOnsite
• Amazon Leadership Principles – Behavioral Deep DiveMedium

  Full Amazon Product Manager onsite behavioral loop: 25 Leadership Principles STAR prompts collected from real interview reports, covering customer obsession, dive deep, data-heavy projects, bias for action, ownership, simplification, conflict, missed commitments, failure, and long-term trade-offs. Each prompt comes with what interviewers score, a metrics-backed model answer, and the pitfalls that sink candidates. Includes a story-bank strategy, a reusable STAR template, and the follow-ups to expect.

  AmazonProduct ManagerBehavioral & LeadershipOnsite
• Amazon New-Service Launch CasesHard

  Practice Amazon new-service launch cases for table booking, flower delivery, and advertising platforms. The solution covers target users, JTBD, MVP scope, architecture, metrics, GTM, risks, Working Backwards framing, and long-term flywheels.

  AmazonProduct ManagerProduct / Decision MakingOnsite
• Analyze Seller Compliance and Customer Purchase PatternsMedium

  This question evaluates a candidate's ability to manipulate and analyze temporal and categorical datasets, covering competencies such as event sequencing, set-based filtering, and cross-table aggregation in SQL/Python.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Analyze Top 10 Items' Revenue Contribution by CategoryMedium

  This question evaluates proficiency in data manipulation and analytical querying—aggregation, window functions, ranking and revenue attribution—alongside conceptual knowledge of normalization versus denormalization and ETL pipeline design.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Analyze an A/B test over last 7 daysHard

  This question evaluates competency in A/B test analysis and experiment design, covering conversion-rate computation, statistical inference (two-proportion testing and confidence intervals), sample-ratio checks, heterogeneity assessment (fixed versus random effects), and power/MDE reasoning.

  AmazonData ScientistAnalytics & ExperimentationOnsite
• Answer Amazon Behavioral QuestionsEasy

  Three classic Amazon final-round behavioral questions — a significant challenge you overcame, motivating a struggling team, and why Amazon — answered with the STAR framework and Amazon Leadership Principles. Each answer emphasizes ownership, problem solving, collaboration, and quantified impact.

  AmazonData EngineerBehavioral & LeadershipTechnical Screen
• Answer Amazon Leadership Principle ScenariosEasy

  This question evaluates behavioral competencies relevant to a Data Scientist intern, including leadership, communication, decision-making under uncertainty, risk assessment, time management, teamwork, learning agility, and motivation for the role.

  AmazonData ScientistBehavioral & LeadershipOnsite
• Answer Amazon Leadership Principles questionsMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Answer Amazon Leadership Principles questions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonSoftware EngineerBehavioral & LeadershipOnsite
• Answer Amazon leadership behavioralsMedium

  Prepare senior Amazon L6 program-management behavioral answers for unconventional problem solving, stakeholder disagreement, ownership outside scope, team speed, and tough feedback. The solution emphasizes STAR structure, mechanisms, leadership principles, and measurable impact.

  AmazonTechnical Program ManagerBehavioral & LeadershipOnsite
• Answer Amazon-style behavioral questionsEasy

  This question evaluates interpersonal leadership, communication, and impact-oriented decision-making for a Data Scientist, testing competencies such as handling workplace challenges, motivating teams, and tying experiences to organizational leadership principles.

  AmazonData ScientistBehavioral & LeadershipTechnical Screen
• Answer Behavioral Questions About Deadlines, Feedback, and AmbiguityMedium

  This question tests your ability to structure past professional experiences using the STAR method (Situation, Task, Action, Result) to demonstrate ownership, adaptability, and emotional intelligence in high-pressure environments.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer Dive Deep and Ownership in LP interviewHard

  This question evaluates a candidate's competency in debugging complex production issues, root cause analysis, operational ownership, cross-team coordination, and incident management within the behavioral and leadership domain.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer Leadership and GenAI Behavioral QuestionsEasy

  This set of prompts evaluates leadership, communication, problem-solving, adaptability, learning agility, and hands-on experience with generative AI, including skills in validating outputs and improving workflows.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer a large batch of variable-ratio queries from division equationsMedium

  Given division equations between named variables and their values, answer a large batch of ratio queries, returning -1.0 whenever a ratio cannot be determined. It tests chaining ratios through shared variables, handling unknown and disconnected variables, and reusing work across many queries, the scale follow-up raised in the interview.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Answer common Amazon behavioral questionsHard

  This question evaluates behavioral competencies and leadership skills, focusing on individual problem-solving, team motivation, communication, and cultural fit through structured storytelling of past experiences.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer common internship behavioral questionsHard

  This question evaluates behavioral competencies including motivation and cultural fit, role-specific interest, decision-making under time pressure, prioritization, and deadline management.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer common leadership promptsMedium

  This question evaluates leadership, communication, teamwork, ownership, feedback handling, problem-solving, and time-management competencies through behavioral examples.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer core behavioral questions for data rolesMedium

  Answer core behavioral questions for data roles evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistBehavioral & LeadershipOnsite
• Answer core probability and inference questionsEasy

  Tests fundamental statistical inference and probability concepts essential for experimentation and data analysis, including the Central Limit Theorem, hypothesis testing errors, and causal reasoning.

  AmazonData ScientistStatistics & MathTechnical Screen
• Answer two coding questions (SQL and DP)Medium

  This problem tests SQL data aggregation skills with NULL handling and algorithmic proficiency in dynamic programming to count palindromic subsequences in a string.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Append country tables and rank salaries in USDMedium

  This question evaluates skills in data manipulation and integration, specifically appending country-level tables, normalizing salaries via historical exchange rates, deduplicating by a composite primary key, and ranking results, with implementations expected in both SQL and pandas.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Architect an AI Chatbot for Enterprise UsersMedium

  This question tests your ability to design a scalable, enterprise-grade AI chatbot system that handles high throughput, ensures data privacy, and integrates with existing business data stores.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Assess Candidate's Alignment with Amazon Leadership PrinciplesMedium

  Evaluates Amazon-style behavioral storytelling for data-science leadership principles such as customer obsession, ownership, dive deep, and earn trust. Strong answers use STAR, metrics, trade-offs, and reflection.

  AmazonData ScientistBehavioral & LeadershipTechnical Screen