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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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- 11
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- 8
- Languages
Showing 5071–5100 of 9403 results
- MetaHardData ScientistDetermining the optimal ad load in News Feed
Evaluates optimal News Feed ad-load strategy using marginal revenue, user tolerance, and long-term LTV. Strong answers test ad-load levels, segment tolerance, personalize caps, and monitor revenue, retention, and user-experience guardrails.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerDiagnose a Single-Server Crash with Concrete Linux Evidence
Walk through a single-server crash investigation using Linux service, kernel, memory, disk, and boot evidence while planning safe recovery.
Software Engineering FundamentalsTechnical Screen - MetaHardData ScientistDiagnose sales correlations without claiming causality
This question tests the ability to design an observational study that identifies associations between a new feature adoption and sales growth while strictly avoiding causal claims in the presence of self-selection bias.
Analytics & ExperimentationTechnical ScreenObservational StudyCausal InferenceAnalytics - MetaHardMachine Learning EngineerDiscuss Projects, Failures, and Growth
This question evaluates interpersonal and leadership competencies—communication, teamwork, conflict resolution, accountability, resilience, and growth mindset—relevant to a Machine Learning Engineer and is categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerDiscuss Research Experience and Challenges
This question evaluates research design, experimental methodology, problem formulation, ownership, collaboration, prioritization, and the ability to learn from failed or ambiguous outcomes within the Machine Learning Engineer role, encompassing communication and leadership competencies.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerDiscuss a Disagreement with a Teammate
This question assesses the candidate's ability to navigate professional conflict, prioritize team goals over personal ego, and maintain effective working relationships through data-driven communication.
Behavioral & LeadershipOnsiteConflict ResolutionCollaborationCommunication - MetaMediumSoftware EngineerDiscuss achievements, ambiguity handling, growth, and conflict management
Discuss achievements, ambiguity handling, growth, and conflict management 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 - MetaMediumSoftware EngineerDiscuss project pride, conflict, and pivots
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Discuss project pride, conflict, and pivots states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - MetaMediumData ScientistEmploy Collaborative Filtering for Personalized Recommendation Lists
Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.
Machine LearningTechnical Screen - MetaMediumSoftware EngineerEngineering Manager Behavioral: Spotting and Closing a Skill Gap on Your Team
An engineering manager behavioral question about realizing that the team lacked a skill it needed. It tests how the gap was noticed and assessed, the trade-offs among training, hiring and borrowing expertise, protecting delivery while people learn, and how the closing of the gap was measured.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerEngineering Manager Behavioral: Struggling Engineers and Sustained Underperformance
A two-part engineering manager behavioral question: helping a team member who was struggling, and handling sustained underperformance. It tests early detection, diagnosing causes with the person, tailored support, specific written expectations, a fair process with HR, and a decisive, respectful outcome.
Behavioral & LeadershipTechnical Screen - MetaMediumData ScientistEnsure Effective Teamwork Amid Conflicting Stakeholder Opinions
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Ensure Effective Teamwork Amid Conflicting Stakeholder Opinions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaHardData ScientistEstimate CTR lift with binomial tests and errors
This question evaluates statistical inference for proportions, sequential monitoring, and multiple-comparison error control—assessing competence with binomial-based CTR comparison, pooled standard errors, z-tests and confidence intervals, Type I error inflation from peeking, and FWER/FDR considerations.
Statistics & MathOnsite - MetaMediumData ScientistEstimate Lift and Significance in Facebook Ad Campaigns
Meta statistics prompt on Facebook ad conversion lift, covering absolute and relative lift, two-proportion tests, confidence intervals, power for 5% relative lift, and Bayesian beta-binomial reframing.
Statistics & MathOnsite - MetaMediumData ScientistEstimate Portal’s causal lift on video-call usage
This problem tests the ability to design an observational causal inference framework using Difference-in-Differences (DiD) in a setting with staggered adoption, specifically addressing selection bias and household-level measurement errors.
Statistics & MathTechnical ScreenCausal InferenceDifference-in-DifferencesStaggered Adoption - MetaHardData ScientistEstimate and validate weights for engagement actions
This question tests the ability to derive an engagement scoring model, handle high-dimensional collinear features, and establish a robust validation framework for point-in-time predictions.
Statistics & MathTechnical ScreenRegressionRegularizationMulticollinearity - MetaMediumData ScientistEstimate bots and CI from DAU spike
This question tests the ability to decompose a mixture of legitimate user traffic and synthetic bot/CI traffic using statistical modeling, and requires justifying the choice of distribution for overdispersed count data.
Statistics & MathOnsiteMixture ModelsNegative BinomialBootstrapping - MetaMediumData ScientistEstimate fake-account prevalence with capture-recapture
This question tests the ability to apply the Lincoln-Petersen estimator to estimate a hidden population size and addresses the limitations of the assumptions underlying capture-recapture methods.
Statistics & MathOnsiteStatisticsEstimationInference - MetaHardData ScientistEstimate revenue of organic shopping tab
This question evaluates a data scientist's skills in revenue modeling, funnel construction, causal inference for bias correction, experimentation scaling, and sensitivity analysis using organic engagement and purchase metrics.
Analytics & ExperimentationOnsite - MetaHardData ScientistEstimate variance for ratio metrics
This question evaluates statistical inference and experimental-analysis skills for data scientists, including variance estimation for ratio metrics via the delta method, covariance estimation from sample moments, model-based comparisons (ANCOVA/log models with offsets), bootstrap and t-test inference considerations, and robustness techniques for heavy-tailed revenue such as winsorization and robust M-estimators. It is commonly asked to assess both theoretical derivation and practical estimation trade-offs in A/B or experimental settings; the category is Statistics & Math and the level of abstraction spans conceptual understanding and practical application.
Statistics & MathOnsite - MetaMediumData ScientistEvaluate AI-assisted ad creation
This question evaluates competency in product analytics, experimentation design, causal inference, metric definition, and measurement of downstream business and user-quality impacts for an AI-assisted advertising feature.
Analytics & ExperimentationOnsite - MetaEasyData ScientistEvaluate AI-assisted ads creation feature
This question assesses the ability to design an A/B test for a new AI feature, focusing on metric definition, experiment design, and mitigating potential trade-offs between advertiser efficiency and ad quality.
Analytics & ExperimentationOnsiteA/B TestingProduct MetricsExperiment Design - MetaMediumData ScientistEvaluate Classifier with Precision, Recall, and Fairness Metrics
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 Classifier with Precision, Recall, and Fairness Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - MetaHardData ScientistEvaluate Facebook Dating launch and validate success
This question evaluates a data scientist's competency in product analytics, experimental design, causal inference, and launch validation by requiring definition of primary and guardrail metrics, test unit and spillover considerations, power/duration planning, behavioral signal expectations, quasi-experimental alternatives, and scale/readiness criteria. Commonly asked in Analytics & Experimentation interviews because organizations need assurance a pilot can be scaled safely and reliably, it tests practical application of experimental methods alongside conceptual understanding of statistical power, spillover risk, diagnostic checks, and operational concerns such as privacy and abuse monitoring.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistEvaluate Factors Before Replacing Recommendation Model
Evaluates whether an ads recommendation model should replace an existing production model. Strong answers combine offline validation, online experiments, revenue and advertiser outcomes, user guardrails, conflicting metric interpretation, rollout safety, rollback planning, and executive communication.
Machine LearningOnsite - MetaMediumData ScientistEvaluate Fake-Account Classifier with Precision and Recall Metrics
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 Fake-Account Classifier with Precision and Recall Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaMediumData ScientistEvaluate Impact of Increasing Stranger Content in Feeds
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 Evaluate Impact of Increasing Stranger Content in Feeds states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluate Instagram's Short-Video Recommender System Success
Evaluates success metrics and experimentation for Instagram's short-video recommender feed. Strong answers choose a durable user-value metric, understand heavy-tailed engagement distributions, handle metric trade-offs, and outline a safe A/B test with retention, safety, creator, and app-level guardrails.
Analytics & ExperimentationOnsite - MetaMediumData ScientistEvaluate Marketing Campaign's Click-Through Rate Effectiveness
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Evaluate Marketing Campaign's Click-Through Rate Effectiveness states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaHardData ScientistEvaluate Messenger's P2P Payments Feature for Business Viability
Evaluates business viability for a Messenger P2P payments feature. Strong answers define networked adoption and transfer metrics, assess fraud, compliance, support, trust, and unit economics, and propose a beta or experiment design that handles spillovers and contamination.
Analytics & ExperimentationOnsite
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