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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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Showing 211–240 of 3640 results
- AmazonMediumApplied ScientistDesigning a Bayesian Classifier for Case Analysis
Tests your ability to design a probabilistic Bayesian classification system under conditions of informative missingness, non-stationary class priors, and strict calibration requirements.
Machine LearningOnsiteBayesian ModelingMissing DataModel Calibration - AmazonMediumSoftware EngineerDetect cycle in a directed graph
This question evaluates mastery of graph algorithms and graph theory concepts—specifically directed cycle detection—and assesses the ability to reason about algorithmic correctness and to analyze time and space complexity for competing approaches.
Coding & AlgorithmsOnsite - 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 - 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 - 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 - 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 - 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 - 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 - AmazonHardData ScientistEvaluate concession gift-card policy with DID
This question evaluates a data scientist's competency in causal inference and experimentation design, encompassing difference-in-differences with staggered adoption and event-study diagnostics, data inventory and unit-of-analysis specification, clustering choices, heterogeneity and guardrail analyses, and cost–benefit and optional randomized experiment design. Commonly asked in Analytics & Experimentation interviews because it gauges the ability to justify identification strategies, diagnose violations of parallel trends and propose alternatives, and translate empirical results into business-impact metrics, it sits at the intersection of conceptual understanding and practical application within the Data Science domain.
Analytics & ExperimentationOnsite - AmazonMediumSoftware EngineerEvaluating Generative AI Use and Accuracy
Tests a candidate's ability to assess when Generative AI is the right solution, choose appropriate architecture patterns (such as RAG or fine-tuning), and rigorously measure and improve model output accuracy in production.
Machine LearningOnsiteGenerative AIRAGML Evaluation - AmazonHardApplied ScientistExplain Collaborative Filtering Approaches
This question evaluates understanding of collaborative filtering approaches and related competencies such as neighborhood-based versus matrix factorization methods, differences for explicit versus implicit feedback, pointwise versus pairwise loss trade-offs, regularization, cold-start and popularity-bias mitigation, evaluation metrics, and scalability considerations. It is asked in the Machine Learning domain to assess both conceptual understanding and practical application for designing, evaluating (e.g., NDCG, MAP) and scaling recommender systems in technical interviews.
Machine LearningOnsite - AmazonMediumSoftware EngineerExplain Decoder-Only LLM Architecture and How vLLM Serves It Efficiently
A two-part conversational question for AI infrastructure roles: explain how a decoder-only transformer turns a prompt into next-token probabilities, then explain how vLLM serves such models efficiently. It tests transformer internals, KV cache sizing, prefill versus decode bottlenecks, paged KV memory and continuous batching.
Machine LearningOnsite - AmazonMediumApplied ScientistExplain Layer Normalization in Transformers
This question evaluates a candidate's understanding of normalization techniques in deep learning—specifically Layer Normalization in Transformer blocks—covering the LayerNorm equation and the roles of gamma, beta, and epsilon, as well as contrasts with BatchNorm and RMSNorm, in the Machine Learning domain.
Machine LearningOnsite - AmazonHardApplied ScientistExplain Logistic Regression Fundamentals
This question evaluates understanding of logistic regression fundamentals, covering probabilistic modeling, Bernoulli likelihood and logit-link derivation, loss and gradient characterization, calibration and miscalibration remedies, regularization trade-offs, feature scaling and interactions, class imbalance strategies, evaluation metrics for skewed data, coefficient interpretation, and common failure modes. It is commonly asked in technical interviews to assess both theoretical and practical competencies in supervised learning within the Machine Learning domain, with a level of abstraction spanning conceptual derivation and practical application of model behavior and evaluation.
Machine LearningOnsite - AmazonMediumApplied ScientistExplain ML evaluation, sequence models, and optimizers
This question evaluates a candidate's competency in machine learning model evaluation and metrics, sequence modeling trade-offs between transformers and RNNs, image distributional shift detection, and comparative understanding of optimization algorithms within the Machine Learning domain.
Machine LearningOnsite - AmazonHardApplied ScientistExplain NLP/RL concepts used in LLM agents
This question tests the ability to synthesize concepts across LLM agent architecture, RAG systems, and reinforcement learning, focusing on architectural design trade-offs and decision-making logic.
Machine LearningOnsiteLLM-AgentsRAGReinforcement-Learning - AmazonMediumSoftware EngineerExplain What You Want in Your Next Role
Explain next-role motivation by connecting personal goals and evidence-backed strengths to verified team needs, while acknowledging open questions and tradeoffs.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerExplain Why You Are Considering a Job Change
This question assesses your ability to articulate a professional narrative that balances growth aspirations with current career satisfaction, ensuring that the motivation for leaving is proactive rather than reactive.
Behavioral & LeadershipOnsiteBehavioralCareer GrowthCommunication - AmazonMediumApplied ScientistExplain XGBoost Parallelism Strategies
This question evaluates a candidate's understanding of parallelism and system-level performance in gradient-boosted tree implementations, covering concepts such as histogram-based split finding, sparse feature handling, cache-friendly data layouts, thread-level work partitioning, and multi-machine data-parallel synchronization.
Machine LearningOnsite - AmazonMediumSoftware EngineerExplain a GenAI Use Case and Learn from a Failure
Tests your ability to critically analyze a production Generative AI architecture, dissect a technical failure or performance degradation, and demonstrate rigorous root-cause analysis alongside validated engineering remediations.
Software Engineering FundamentalsOnsiteGenerative AISystem DesignRCA - AmazonMediumSoftware EngineerExplain how you use AI tools to learn and code, and defend a past AI project
A hiring-manager discussion for an entry-level software engineer about working with AI tools. It asks how the candidate uses AI to learn and to code, then probes a past AI project: whether AI was truly needed, how its output is verified, and how it would scale in a very large organization.
Behavioral & LeadershipOnsite - AmazonHardApplied ScientistExplain key ML theory and techniques
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 key ML theory and techniques states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - AmazonMediumApplied ScientistExplain parallelism and collectives in training
This question evaluates a candidate's competency in designing scalable distributed training systems, covering parallelism strategies (data, model/tensor, pipeline), communication collectives (all-reduce, all-gather, reduce-scatter, broadcast), and tensor-level layer partitioning such as column- and row-parallel splits.
ML System DesignOnsite - AmazonMediumApplied ScientistExplain why CTR rises but CVR unchanged
This question tests the ability to diagnose Simpson's Paradox or composition shifts in funnel metrics when engagement increases without a corresponding lift in final conversion.
Analytics & ExperimentationOnsiteAnalyticsExperimentationFunnel Analysis - AmazonMediumData ScientistExplore Dataset to Assess Quality and Choose Visualizations
Evaluates fast dataset exploration, data-quality assessment, and visualization planning for business stakeholders. Strong answers profile schema, check missingness and bias, and choose charts aligned to decisions.
Analytics & ExperimentationOnsite - AmazonMediumBusiness Intelligence EngineerFind Top Three Books by City in Recent Months
This question evaluates proficiency in time-windowed aggregation, group-level ranking and window-function usage on relational datasets, emphasizing handling of recent date ranges and top-N selection.
Data Manipulation (SQL/Python)Onsite
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