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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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Showing 571–600 of 9403 results
- 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 - AmazonMediumSoftware EngineerDetermine string segmentability with dictionary
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Determine string segmentability with dictionary states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistDiagnose Bias–Variance Trade-off in Supervised Learning
Diagnose Bias–Variance Trade-off in Supervised Learning evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - AmazonMediumData ScientistDiagnose Business Decline Using Key Data Metrics
Diagnose Business Decline Using Key Data Metrics 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 & ExperimentationOnsite - 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 - AmazonMediumData ScientistDiagnose MySQL joins and GROUP BY/HAVING errors
This question evaluates mastery of SQL join semantics (LEFT, RIGHT, FULL OUTER emulation, CROSS), NULL equality and duplicate handling, GROUP BY/HAVING behavior under ONLY_FULL_GROUP_BY, aggregation result shaping and type/format conversions in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Online Assessment - AmazonHardSoftware EngineerDiagnose Return Eligibility Scoring Bugs
This question evaluates debugging and root-cause analysis skills related to scoring logic, data integrity, feature engineering, and system reliability in return-eligibility services.
Software Engineering FundamentalsOnline Assessment - AmazonMediumSoftware EngineerDigging Into Details: Untangling a Complex Team Problem and Finding a Root Cause
Two behavioral prompts on diving deep: describe untangling a complex team problem by examining the details yourself, and finding the true root cause behind a misleading first explanation. It assesses hands-on investigation, rigor with evidence and fixes that prevent recurrence.
Behavioral & LeadershipOther - AmazonEasySoftware EngineerDiscuss AI Use, Deadlines, Ambiguity, and Feedback
Prepare evidence-based behavioral stories about generative AI use, technical complexity, deadlines, ambiguity, ownership, critical feedback, and rapid learning. Emphasize personal decisions, verification, stakeholder communication, honest trade-offs, measurable outcomes, and lessons from imperfect results.
Behavioral & LeadershipOnsite - 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 - AmazonMediumFrontend EngineerDrive an Improvement Beyond Your Formal Responsibilities
This question assesses a candidate's ability to proactively identify technical or operational inefficiencies, influence stakeholders without formal authority, and implement measurable, sustainable improvements.
Behavioral & LeadershipOnsiteLeadershipInitiativeCollaboration - AmazonHardData ScientistDrive stakeholder alignment under trade-offs
This question evaluates competency in cross-functional stakeholder alignment, trade-off analysis, data governance and security risk assessment, executive communication, and program decision-making within a technical product context.
Behavioral & LeadershipOnline Assessment - AmazonMediumSoftware EngineerDrone Circular Route — Minimum Total Travel Cost
This problem tests your ability to solve a variation of the Traveling Salesman Problem using bitmask dynamic programming to optimize route costs in a small, weighted graph.
Coding & AlgorithmsOnline AssessmentDynamic ProgrammingBitmaskingGraph Theory - 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 - AmazonMediumSoftware EngineerEscalation Stack That Resolves the Most-Reported Issue First
Design a support-escalation structure where resolving an issue always clears whichever one currently has the most open reports, breaking ties by the most recently reported occurrence. Tests combining frequency and recency so both operations run in O(1).
Coding & AlgorithmsOnsitestackhash-mapdesign - AmazonHardData ScientistEstimate Treatment Effects Using PSM, DiD, and DML Methods
Estimate Treatment Effects Using PSM, DiD, and DML Methods evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - AmazonHardData ScientistEstimate live sports impact on subscriptions
This question evaluates a data scientist's competence in causal inference and observational study design within analytics and experimentation, covering skills such as defining treatment and control, selecting outcome metrics and covariates, and choosing identification strategies like matching, IV, and difference-in-differences; it is commonly asked to assess the ability to produce credible causal estimates when randomized experiments are unavailable. It tests both conceptual understanding of identification assumptions (e.g., unconfoundedness, overlap, SUTVA, IV validity) and practical application of matching algorithms, propensity modeling, regression specifications, diagnostics, and staggered-rollout/event-study designs for real-world program evaluation.
Analytics & ExperimentationOnsite - 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 - AmazonEasyData ScientistEvaluate NLP Classification Models
This question evaluates competency in NLP classification model evaluation, covering understanding of confusion matrices, precision/recall/F1/AUC metrics, thresholding and trade-offs, handling class imbalance, and connecting metric choices to operational actions and business costs.
Machine LearningOnsite - 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 - AmazonMediumData ScientistEvaluate Soft Skills Through Behavioral Interview Questions
Evaluates behavioral soft skills through data-science leadership prompts about helping teammates, diving deep, deadlines, independent decisions, and growth. Strong answers use STAR with concrete actions and outcomes.
Behavioral & LeadershipOnsite - AmazonHardSoftware EngineerEvaluate actions in Amazon simulation
Evaluate actions in Amazon simulation 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 & LeadershipOnline Assessment - 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
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