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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.
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- 546
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- 11
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Showing 271–300 of 4616 results
- AmazonMediumData ScientistDetermine Discount's Effect on Conversion Rate with A/B Testing
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Discount's Effect on Conversion Rate with A/B Testing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - AmazonMediumData ScientistDetermine Length of Longest Unique Substring
This question evaluates string-processing skills and algorithmic problem-solving, including handling character uniqueness and performance considerations. Commonly asked in Coding & Algorithms interviews to assess a candidate's ability to implement efficient solutions and reason about time/space trade-offs, it targets practical application rather than purely conceptual understanding.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistDetermine Probability of Both Children Being Boys
Evaluates conditional probability reasoning in the classic two-children problem with different observation mechanisms. Strong answers enumerate ordered outcomes, condition correctly, and compare at-least-one, older-child, and random-child cases.
Statistics & MathTechnical Screen - AmazonMediumSoftware EngineerDetermine Whether All Courses Can Be Completed
This problem tests your ability to model dependencies using a directed graph and detect cycles to determine if a set of tasks can be completed.
Coding & AlgorithmsTechnical ScreenGraphTopological SortDFS - AmazonEasySoftware EngineerDetermine Whether One Person Can Attend Every Meeting
This problem tests your ability to handle interval sorting and linear-time overlap detection to ensure no two meetings conflict.
Coding & AlgorithmsTechnical ScreenSortingIntervalsGreedy - AmazonMediumSoftware EngineerDetermine Whether Package Weights Can Be Safely Arranged
Tests array manipulation, counting techniques, and greedy or constraint-satisfaction logic to determine if an array can be permuted so no two adjacent elements sum to ten or more.
Coding & AlgorithmsTechnical ScreenGreedyCountingArray - AmazonMediumSoftware EngineerDetermine Whether a Binary Tree Is a Valid BST
Determine whether an entire breadth-first encoded binary tree obeys the strict BST invariant. Use ancestor bounds rather than only parent-child checks, reject duplicates, handle numeric extremes, and discuss an iterative approach for deep trees.
Coding & AlgorithmsTechnical Screen - AmazonMediumApplied ScientistDetermine if all courses can be completed
This problem tests your ability to model prerequisites as a directed graph and detect cycles to determine if a valid topological ordering exists.
Coding & AlgorithmsTechnical ScreenGraphsTopological SortDFS - AmazonMediumData ScientistDiagnose Causes and Test Hypotheses for Metric Drop
Evaluates incident-style analysis of a sudden purchase-conversion drop in a consumer product. Strong answers list plausible causes, validate with concrete cuts and logs, design an A/B test, and define metrics.
Analytics & ExperimentationTechnical Screen - AmazonEasySoftware EngineerDiscuss Feedback, User Needs, Influence, Deadlines, and Learning
This set of behavioral prompts evaluates a candidate's ability to navigate high-pressure engineering environments, focusing on self-awareness, cross-functional collaboration, and technical growth.
Behavioral & LeadershipTechnical ScreenLeadershipSoft SkillsAdaptability - AmazonMediumSoftware EngineerDiscuss deadline, challenge, feature
Discuss deadline, challenge, feature 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 & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDiscuss deadlines, feedback, and deep dives
An Amazon software engineer behavioral screen covering deadline and prioritization skills, accountability for a missed deadline, response to negative feedback, going beyond requirements, root-cause deep dives with verification, and thoughtful reverse questions for the hiring manager. Includes STAR(L) sample answers and a scoring rubric tied to Amazon's Leadership Principles.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDiscuss open-source contributions and impact
This question evaluates a software engineer's competency in technical communication, ownership and design trade-offs, collaborative contribution workflows (issues, pull requests, and reviews), measurable impact reporting, and ongoing maintenance within open-source projects.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDive Deep to Find the Root Cause of a Problem
This question tests your structured problem-solving approach, technical depth, and ability to challenge initial assumptions when troubleshooting complex production issues.
Behavioral & LeadershipTechnical ScreenBehavioralProblem SolvingRoot Cause Analysis - AmazonMediumSoftware EngineerDive Deep to Resolve a Difficult Problem
Tell how you investigated beyond an appealing first explanation to resolve a difficult problem. Present misleading symptoms, at least one rejected hypothesis, evidence that narrowed the cause, verification, and a durable prevention or detection improvement.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistEnsure Data Quality and Deliver Impact Amid Challenges
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Ensure Data Quality and Deliver Impact Amid Challenges states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistEstimate shuttle impact with robust causal design
This question evaluates a candidate's competency in causal inference and experimental analytics, covering staggered-adoption difference-in-differences design, estimand and outcome definition, parallel-trends diagnostics, selection and time-varying confounding considerations, clustering and weighting choices, handling varied adoption timing and site attrition, robustness checks, and communication of coefficient interpretation and uncertainty. It is commonly asked in the Analytics & Experimentation domain because it tests both conceptual understanding of identification assumptions and practical application of statistical design choices for real-world observational causal analysis.
Analytics & ExperimentationTechnical Screen - AmazonHardSoftware EngineerEvaluate IP Access Rules
This question evaluates knowledge of IPv4 addressing, CIDR subnet matching, and access-control rule resolution, assessing competency in bitwise address reasoning, prefix-length precedence, and rule tie-breaking.
Coding & AlgorithmsTechnical Screen - AmazonHardData ScientistEvaluate RAG System Accuracy and Cost Control Strategies
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Evaluate RAG System Accuracy and Cost Control Strategies states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonEasySoftware EngineerEvaluate an AI-Assisted Engineering Workflow
This question tests the candidate's ability to critically analyze the integration of AI tools into the software development lifecycle by balancing productivity gains with code quality and reliability.
Software Engineering FundamentalsTechnical ScreenAI-EngineeringWorkflow-DesignCode-Migration - AmazonMediumProduct ManagerEvaluate delivery driver performance
Evaluate delivery driver performance with a fair Amazon-style scorecard that goes beyond package count and delivery time. The answer covers route normalization, customer experience, safety guardrails, weather adjustment, driver coaching, and operational root-cause analysis.
Product Design & StrategyTechnical Screen - AmazonHardSoftware EngineerExplain Aurora-style internals: WAL, MVCC, replication, recovery
This question tests your conceptual understanding of how log-structured storage engines, like Amazon Aurora, decouple the database log from the data pages to achieve low-latency replication and fast recovery.
Software Engineering FundamentalsTechnical ScreenDistributed SystemsDatabase InternalsStorage Engines - AmazonHardApplied ScientistExplain Core ML Interview Concepts
This question evaluates core machine learning fundamentals including statistical modeling assumptions and loss functions (linear and logistic regression), ensemble methods and feature sampling in random forests, optimization algorithms (Adam versus stochastic gradient descent), and neural network capacity and training dynamics.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerExplain Customer-Focused Technical Work and AI Verification
This question assesses a candidate's ability to balance rapid AI adoption with rigorous engineering standards, focusing on ownership and customer impact.
Behavioral & LeadershipTechnical ScreenBehavioralAISystem Design - AmazonMediumData ScientistExplain How You Handled a Missed Commitment
Prepare a behavioral example about a missed deadline, stakeholder communication, recovery, and improved commitment management.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistExplain K-Fold Cross-Validation and Its Trade-Offs
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain K-Fold Cross-Validation and Its Trade-Offs states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonMediumApplied ScientistExplain LLM architecture, tuning, evaluation
This question evaluates understanding of transformer-based LLM architectures, positional embedding variants, parameter-efficient fine-tuning (PEFT) approaches, regularization strategies, and evaluation methodologies within the Machine Learning domain focused on large language models and NLP, emphasizing both conceptual understanding and practical application. It is commonly asked to assess the interviewee’s ability to reason about architecture and tuning trade-offs, model generalization and stability, and the design of reliable offline and online evaluation pipelines for production-minded language model systems.
Machine LearningTechnical Screen - AmazonHardApplied ScientistExplain ML statistics and model design concepts
Explain ML statistics and model design concepts 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 DesignTechnical Screen - AmazonMediumData ScientistExplain Overfitting and Underfitting in Machine Learning
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Overfitting and Underfitting in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonMediumData ScientistExplain P-value, Confidence Interval, and Multiple Testing Adjustments
Evaluates A/B testing inference fundamentals, including p-values, confidence intervals, multiple testing adjustments, Type I and Type II errors, z-tests versus t-tests, and CLT versus LLN. Strong answers connect definitions to experiment decisions and common pitfalls.
Statistics & MathTechnical Screen
Amazon