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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 2041–2070 of 3640 results
- 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 - 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 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 - 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 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 - MetaHardData ScientistEvaluate New Ad Model with A/B Testing Experiment
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 New Ad Model with A/B Testing Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistEvaluate Probability of Positive User Comments and Model Performance
Evaluate Probability of Positive User Comments and Model Performance 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 - MetaMediumData ScientistEvaluate a New Ads-Ranking Algorithm
This question tests the ability to design a robust A/B test for an ads-ranking system while accounting for interference effects, budget constraints, and conflicting metric signals.
Analytics & ExperimentationOnsiteA/B TestingAd-TechMetrics - MetaMediumSoftware EngineerEvaluate arithmetic expression without parentheses
Evaluate arithmetic expression without parentheses evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - MetaMediumData ScientistEvaluate fake accounts and ad creation
This question tests the ability to design an evaluation framework for identifying malicious actors in an ad-tech environment and measuring the performance of automated content generation tools.
Analytics & ExperimentationOnsiteAnalyticsExperimentationMetric Design - MetaHardData EngineerEvaluate impact of short videos in feed
This question evaluates competency in metrics definition, causal experimentation, product analytics, and feed system behavior within the System Design and Data Engineering domain.
System DesignOnsite - MetaHardData ScientistEvaluate the Health of Facebook Groups
Evaluates Facebook Group health metrics and threaded-comments experiment design. Strong answers define normalized health scores, compare small and large groups fairly, and test deep interaction with safety guardrails.
Analytics & ExperimentationOnsite - MetaMediumSoftware EngineerExecute Dependency-Constrained Work at Scale
This problem tests the ability to design an execution engine that manages task dependencies using a directed acyclic graph (DAG), respects concurrency limits, and handles task failures.
Software Engineering FundamentalsOnsiteConcurrencyGraphSystem Design - MetaMediumSoftware EngineerExplain ACID and isolation levels
This question evaluates a candidate's knowledge of database transactions, the ACID properties (Atomicity, Consistency, Isolation, Durability), and common isolation levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable), testing competency in concurrency control and data consistency.
Software Engineering FundamentalsOnsite - MetaEasyData ScientistExplain Central Limit Theorem's Importance in A/B Testing
Evaluates understanding of the Central Limit Theorem and confidence intervals in A/B testing. Strong answers explain sampling distributions, standard error, normal approximations for experiment metrics, caveats for dependence and heavy tails, and compute the 95% confidence interval as [2.902, 3.098].
Statistics & MathOnsite - MetaMediumSoftware EngineerExplain Collaboration, Ambiguity, and Prioritization
This question assesses a candidate's ability to navigate cross-functional friction, synthesize ambiguous project requirements into actionable tasks, and demonstrate maturity in making trade-offs under pressure.
Behavioral & LeadershipOnsiteBehavioralLeadershipStakeholder Management - MetaMediumSoftware EngineerExplain Your Highest-Impact Project
Tests an experienced engineer's ability to articulate business and technical impact, demonstrate end-to-end ownership, justify complex architectural trade-offs, and quantify measurable outcomes.
Behavioral & LeadershipOnsiteBehavioralLeadershipImpact - MetaMediumSoftware EngineerExplain a Failure and What You Changed
This question evaluates your ability to take personal ownership of a significant technical or project failure, analyze root causes without deflecting blame, and implement lasting systemic improvements.
Behavioral & LeadershipOnsiteBehavioralLeadershipPost-Mortem - MetaMediumSoftware EngineerExplain behavioral experiences and decisions
Explain behavioral experiences and decisions 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
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