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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
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- 546
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- 11
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- 8
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Showing 301–330 of 9403 results
- AmazonMediumSoftware EngineerAnswer Behavioral Questions About Deadlines, Feedback, and Ambiguity
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.
Behavioral & LeadershipTechnical ScreenBehavioralLeadershipCommunication - AmazonHardSoftware EngineerAnswer Dive Deep and Ownership in LP interview
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.
Behavioral & LeadershipTechnical Screen - AmazonEasySoftware EngineerAnswer Leadership and GenAI Behavioral Questions
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.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerAnswer a large batch of variable-ratio queries from division equations
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.
Coding & AlgorithmsTechnical Screen - AmazonHardSoftware EngineerAnswer common Amazon behavioral questions
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.
Behavioral & LeadershipTechnical Screen - AmazonHardSoftware EngineerAnswer common internship behavioral questions
This question evaluates behavioral competencies including motivation and cultural fit, role-specific interest, decision-making under time pressure, prioritization, and deadline management.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerAnswer common leadership prompts
This question evaluates leadership, communication, teamwork, ownership, feedback handling, problem-solving, and time-management competencies through behavioral examples.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistAnswer core behavioral questions for data roles
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.
Behavioral & LeadershipOnsite - AmazonEasyData ScientistAnswer core probability and inference questions
Tests fundamental statistical inference and probability concepts essential for experimentation and data analysis, including the Central Limit Theorem, hypothesis testing errors, and causal reasoning.
Statistics & MathTechnical ScreenStatisticsA/B TestingHypothesis Testing - AmazonMediumSoftware EngineerAnswer two coding questions (SQL and DP)
This problem tests SQL data aggregation skills with NULL handling and algorithmic proficiency in dynamic programming to count palindromic subsequences in a string.
Coding & AlgorithmsTechnical ScreenSQLDynamic ProgrammingAggregation - AmazonMediumData ScientistAppend country tables and rank salaries in USD
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.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerArchitect an AI Chatbot for Enterprise Users
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.
System DesignTechnical ScreenRAGScalabilityAccess Control - AmazonMediumData ScientistAssess Candidate's Alignment with Amazon Leadership Principles
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.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistAssess Culture Fit Through Behavioral Interview Questions
Evaluates culture-fit behavioral answers for inventing and simplifying, customer communication, and innovation. Strong answers use STAR, show stakeholder judgment, quantify impact, and reflect on lessons.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerAssess cooldown and plan interview strategy
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess cooldown and plan interview strategy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AmazonHardProduct ManagerBehavioral Deep-Dive & Leadership Scenarios
Practice behavioral deep-dive and leadership scenarios for Product Managers, including high-impact projects, cross-functional programs, complex launches, process improvement, strategy focus, influence, disagreement, limited-data decisions, leadership principles, and input metrics.
Behavioral & LeadershipTechnical Screen - AmazonMediumProduct ManagerBehavioral Problem-Solving Scenarios
Practice Amazon Product Manager behavioral scenarios with structured STAR examples for data-driven problem solving, complex constraints, difficult customers, fast decisions, and ownership beyond scope. The solution emphasizes metrics, trade-offs, guardrails, reusable mechanisms, and interview-ready answer patterns.
Behavioral & LeadershipOnsite - AmazonMediumProduct ManagerBehavioral: Ambiguity & Conflict Resolution
Practice behavioral PM answers about delivering results amid ambiguity and resolving principled disagreement. The guide uses STAR examples to show customer problem framing, metrics, experiments, decision docs, stakeholder alignment, respectful conflict, disagree-and-commit behavior, and measurable outcomes.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerBehavioral: Resolving a Workplace Conflict and Using Generative AI in Your Work
A two-part behavioral question from a software engineering onsite: describe a conflict with a coworker and how it was resolved, then explain how you have used generative AI in your work. It tests ownership, settling disagreements with evidence, verifying AI output, and judgment about the limits of AI tools.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerBrainstorm a business problem approach
Brainstorm a business problem approach evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - AmazonMediumFrontend EngineerBuild Accessible Data-Loading Popovers for Multiple Buttons
This problem tests the ability to build a robust, accessible UI component that manages asynchronous data fetching, state synchronization, and DOM-level focus management.
Software Engineering FundamentalsOnsiteFrontendReactAccessibility - AmazonMediumData ScientistBuild DID panel and compute effects in SQL
This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.
Data Manipulation (SQL/Python)HR Screen - AmazonMediumData ScientistBuild DiD dataset with SQL
This question evaluates proficiency in SQL-based data manipulation and panel construction for staggered-adoption difference-in-differences, testing skills in handling adoption dates, event-time calculations, boundary conditions, and aggregation in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerBuild a BFS Web Crawler
This question evaluates understanding of breadth-first traversal, URL normalization, domain scoping, duplicate detection, and retry/error handling, measuring algorithmic design and robustness in networked code.
Coding & AlgorithmsTechnical Screen - AmazonHardSoftware EngineerBuild a Searchable Sentence Index
This problem tests your ability to design an in-memory inverted index for text retrieval, requiring efficient tokenization, case-insensitive mapping, and dynamic updates to a collection of sentences.
Coding & AlgorithmsOnsiteHash MapsInverted IndexData Structures - AmazonMediumSoftware EngineerBuild a Solution That Benefits Teams Beyond Your Own
This question assesses a candidate's ability to think beyond immediate team requirements, identifying systemic inefficiencies and designing scalable solutions that provide cross-functional value.
Behavioral & LeadershipOnsiteLeadershipCross-functionalScalability - AmazonHardData ScientistBuild a package-allocation model for couriers
This question evaluates a candidate's ability to design an end-to-end ML-driven package-to-courier allocation system, testing competencies in predictive modeling (per-stop service-time forecasting), constrained combinatorial optimization for routing and fairness, feature engineering, simulation, and production concerns such as cold-start handling and distribution shift. It is asked to assess how candidates balance competing objectives like on-time delivery versus fairness under real-world constraints (shift hours, vehicle capacity, delivery windows, time-varying travel forecasts), and is categorized under Machine Learning with a focus on practical application-level system design rather than purely conceptual theory.
Machine LearningOnsite - AmazonMediumSoftware EngineerBuild a responsive grid of fixed boxes
This problem tests your proficiency in creating responsive UI layouts using CSS Flexbox or Grid and your ability to optimize rendering performance for large datasets using virtualization or efficient list rendering.
Software Engineering FundamentalsTechnical ScreenReactCSS GridFrontend - AmazonHardSoftware EngineerBuild an LRU Cache with a Hash Map and Linked List
This problem tests your ability to design a cache with O(1) time complexity for both get and put operations by combining a hash map with a doubly linked list.
Coding & AlgorithmsHR ScreenHash MapLinked ListDesign - AmazonHardApplied ScientistBuild an end-to-end ML pipeline
Build an end-to-end ML pipeline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite
Amazon