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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 5101–5130 of 9403 results
- 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 Notification-Based Account Ranking
This question tests the ability to design an experiment to measure the causal impact of a new notification strategy on account ranking quality and user engagement.
Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMetrics - 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 Live-Stream Group Notification Under Network Effects
This question tests the ability to design an experiment that accounts for spillover effects and user fatigue in a social graph, while proposing causal inference methods for environments where randomization is restricted.
Analytics & ExperimentationTechnical ScreenCausal InferenceNetwork EffectsExperimentation - 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 - MetaMediumSoftware EngineerEvaluate expression without stack, constant space
Evaluate expression without stack, constant space 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 & AlgorithmsTechnical Screen - 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 shopping tab pre- and post-launch
This question evaluates a data scientist's competency in causal inference, experiment design, funnel metric instrumentation, revenue modeling, sensitivity analysis, and data‑quality triage within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - 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 - MetaMediumData ScientistEvaluating a 15 % reduction in post‑card height
Evaluates feed UX experimentation for reducing post-card height and diagnosing divergent geographic revenue outcomes. Strong answers measure scroll efficiency, viewability, engagement, ad revenue, device/network effects, and geo-specific decisions.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating the Facebook ‘Memory’ feature
Evaluates Facebook Memories feature performance beyond click-through rate and surface-level engagement. Strong answers measure incremental engagement, retention, cannibalization, quality guardrails, frequency caps, sensitive-content handling, and launch criteria.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating the Impact of Duplicate and Stolen Posts on a Content Platform
This question tests the ability to design a measurement framework for evaluating content-quality problems on a large user-generated-content platform. It assesses competency in metric selection, experimentation design, and causal reasoning within the Analytics & Experimentation domain — skills central to data science roles focused on platform health.
Analytics & ExperimentationTechnical Screen - 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 - MetaMediumData ScientistExplain Your Motivation for Career Transition and Role Interest
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain Your Motivation for Career Transition and Role Interest states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - 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 - MetaEasyProduct AnalystExplain a project’s impact and product thinking
This question evaluates a product analyst's product thinking, analytical rigor, experiment design and execution, metric selection and trade-off analysis, and ability to quantify and communicate project impact while demonstrating leadership in end-to-end ownership.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerExplain a recent project and measured impact
This question evaluates project leadership, technical decision-making, trade-off analysis, cross-functional influence, and the ability to quantify impact with metrics.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerExplain an AI System You Designed and the Problem It Solved
Tests your ability to articulate an end-to-end production AI/ML system you designed, detailing the business justification, technical architecture, evaluation strategy, failure modes, and your individual contributions.
ML System DesignTechnical Screenml-system-designai-architecturemodel-evaluation - 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 - MetaHardSoftware EngineerExplain the Project You Are Most Proud Of
This question tests your ability to articulate a complex technical contribution, demonstrate ownership through adversity, and communicate impact clearly to a non-expert interviewer.
Behavioral & LeadershipOnsiteBehavioralLeadershipOwnership - MetaEasyData ScientistExplain why IG Story usage exceeds Facebook
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 Explain why IG Story usage exceeds Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumSoftware EngineerExplain your main growth area
This question evaluates a candidate's self-awareness, growth mindset, and ability to identify and develop specific professional skills or behaviors relevant to leadership, collaboration, and technical role effectiveness.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerExtend BFS maze solver with keys and arrows
This problem tests your ability to adapt Breadth-First Search (BFS) to a state-space where the current location is augmented by a collection of keys, effectively transforming the grid into a larger, multi-layered graph.
Coding & AlgorithmsOnsiteBFSGraphsBitmask - MetaMediumSoftware EngineerExtend a Maze Solver
This question evaluates algorithmic problem-solving and software engineering skills, specifically graph traversal (BFS), state-space modeling for keys and doors, handling movement constraints, and debugging an existing codebase within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen
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