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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
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 2011–2040 of 3640 results
- MetaHardSoftware EngineerDesign and Evaluate a Solver for a 36-Card Sum-Fifteen Game
Tests state-space search (DFS with memoization), heuristic design, and simulation benchmarking for a deterministic 36-card solitaire game.
Software Engineering FundamentalsOnsiteBacktrackingGame TheoryHeuristics - MetaHardData ScientistDesign and evaluate P2P payments in messaging
This question evaluates a data scientist's competency in product analytics, causal inference, experimental design under network interference, monetization measurement (ARPU impact), and risk mitigation for a peer-to-peer payments feature relevant to a Data Scientist role.
Analytics & ExperimentationOnsite - MetaEasyAnalytics EngineerDesign and evaluate an ads ranking algorithm
This problem tests your understanding of the end-to-end machine learning lifecycle for an ads ranking system, focusing on feature engineering, model selection, and the transition from offline metrics to online business impact.
Machine LearningOnsiteAds RankingMetricsEvaluation - MetaMediumData ScientistDesign and validate ad model launch
This question evaluates competence in experimental design, causal inference, online A/B testing and launch decision frameworks for ad recommendation systems, covering randomization/unit choice, metric definition and guardrails, power and duration estimation, validity checks, monitoring and visualization in the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign and validate an ads feed experiment
This question tests the ability to design a rigorous A/B test for a core product feature, covering metric selection, interference handling, and post-experiment analysis under complex constraints.
Analytics & ExperimentationOnsiteA/B TestingCausal InferenceProduct Metrics - MetaHardData EngineerDesign batch and streaming ETL architecture
Design batch and streaming ETL architecture evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - MetaMediumSoftware EngineerDesign coding platform with global leaderboard
This question tests the ability to design a high-throughput, real-time leaderboard system that balances strong consistency for rankings with the scalability required for a global user base.
System DesignOnsiteScalabilityCachingDatabase Partitioning - MetaMediumMachine Learning EngineerDesign comment ranking for a news feed
This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.
ML System DesignOnsiteRankingRecommendation SystemsMulti-task Learning - MetaHardData ScientistDesign experiments under network interference
This question evaluates a data scientist's competence in experimental design and causal inference under network interference, assessing understanding of clustering strategies, exposure and exclusion definitions, contamination control, design effect and sample-size implications, estimation of intra-cluster correlation, and mixed-effects analysis for evolving clusters. Commonly asked in Analytics & Experimentation interviews to probe the ability to preserve internal validity while managing operational feasibility in two-sided marketplaces, it tests both conceptual understanding of interference and practical application skills in design and analysis planning.
Analytics & ExperimentationOnsite - MetaEasyData ScientistDesign metrics to detect harmful content and fraud
This problem tests the ability to design robust monitoring metrics for detecting fraudulent user activity and policy-violating content, focusing on trade-offs between precision and recall.
Analytics & ExperimentationOnsiteAnalyticsMetricsFraud-Detection - MetaHardMachine Learning EngineerDesign nearby place recommendations
This question evaluates skills in designing real-time, large-scale machine learning recommender systems that combine geospatial candidate generation, feature engineering, ranking models, personalization, privacy controls, fraud detection, and operational constraints like latency and throughput.
ML System DesignOnsite - MetaHardSoftware EngineerDesign place-of-interest ML system
Design place-of-interest ML system evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign real-time live comments
This question evaluates the candidate's ability to design scalable, low‑latency real‑time messaging systems, focusing on high‑fanout delivery, approximate time ordering, moderation and rate‑limiting mechanisms, and replay of recent events.
System DesignOnsite - MetaHardMachine Learning EngineerDesign scalable media storage and delivery
This question evaluates an engineer's ability to design globally distributed, highly scalable media storage and delivery systems, covering competencies in object storage and metadata modeling, CDN integration, replication and consistency strategies, security and access control, background processing, observability, quotas, and cost/performance trade-offs. It is commonly asked in system design interviews to assess reasoning about durability, availability, latency and cost trade-offs while requiring both high-level architectural thinking and practical implementation details; category: System Design; level of abstraction: combines conceptual architecture with practical application considerations.
System DesignOnsite - MetaHardSoftware EngineerDesign searchable status & geolocation services
Design searchable status & geolocation services evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - MetaMediumSoftware EngineerDesign structure for top-K frequent elements
This question evaluates a candidate's competency in algorithm and data-structure design, frequency counting and streaming/approximation techniques, and the ability to reason about time/space complexity and workload-driven trade-offs.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerDesign the Frontend Data Flow and APIs for a Social Media News Feed
A product architecture design problem: starting from a feed mock-up, design the client data flow and APIs for a social news feed on web and mobile. It tests UX flow analysis, the shape of the feed response object, cursor-based pagination, and edge cases across client types.
System DesignOnsite - MetaMediumSoftware EngineerDesign ticketing system with seat hold
This question evaluates a candidate's skills in system design, distributed systems, concurrency control, data modeling, and scalability, testing their ability to reason about correctness and performance under high contention and heavy traffic as well as trade-offs between consistency and availability.
System DesignOnsite - MetaMediumMachine Learning EngineerDesign weapon-selling ad detection from posts
This question tests the ability to design an end-to-end multimodal classification system for detecting policy-violating content (weapon sales) while balancing latency, precision, and robustness against adversarial evasion.
ML System DesignOnsiteComputer VisionNLPMultimodal Learning - MetaMediumData ScientistDetect and address Simpson’s paradox
This question evaluates understanding of aggregation bias and Simpson's paradox, proficiency with stratified or mixed-model estimators, heterogeneity testing, and experiment decisioning within Analytics & Experimentation for a Data Scientist role.
Analytics & ExperimentationOnsite - MetaEasyAnalytics EngineerDetect fake accounts and measure their impact
This question tests the ability to design an analytical framework for identifying fraudulent behavior and quantifying the financial or engagement impact of those accounts on a platform.
Analytics & ExperimentationOnsiteFraud DetectionCausal InferenceMetrics - MetaHardData ScientistDetect leakage and evaluate a prediction model
This question evaluates competence in production-ready supervised machine learning—detecting and repairing data leakage in logs-based features, designing time-aware cross-validation, defining KPI-driven thresholding and calibration, interpreting model coefficients, and establishing monitoring and retraining policies for a churn prediction model in the Machine Learning domain. It is commonly asked because interviewers need to assess both conceptual understanding of leakage, validation, and calibration principles and practical application skills for deploying and maintaining models with cost-sensitive objectives and monitoring in production, so the level of abstraction spans conceptual reasoning and hands-on practical application.
Machine LearningOnsite - MetaHardData ScientistDetermine Key Metrics and Design A/B Test for Ad Ranking
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 Determine Key Metrics and Design A/B Test for Ad Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDetermine Metrics to Evaluate Notification Impact on Users
Evaluates product analytics judgment for Facebook push notifications, including how to measure helpful versus harmful notifications, design an event-notification experiment, and balance short-term clicks against retention, fatigue, and opt-out guardrails. Strong answers build a metric hierarchy and use causal measurement.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDetermine Old vs. New Users' Shop Visibility Changes
This question evaluates SQL-focused data manipulation skills and analytical competency in cohort definition and metric selection for comparing behavioral propensities, specifically assessing how to measure the likelihood of old versus new users switching a shop profile to invisible.
Data Manipulation (SQL/Python)Onsite - MetaEasyData ScientistDetermine Posterior Probability of Bad User Prediction
Meta Bayes theorem question on a bad-actor classifier, covering prevalence, sensitivity, specificity, false positives, positive predictive value, and the base-rate effect.
Statistics & MathOnsite - MetaMediumData ScientistDetermine Probability of Fourth Good Response After Three Successes
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine Probability of Fourth Good Response After Three Successes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaEasyData ScientistDetermine Probability of Friend Request Being Fake
Determine Probability of Friend Request Being Fake 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 ScientistDetermine Unhealthy Oculus Usage with SQL Analysis
This question evaluates SQL-based data manipulation and time-series analysis competencies, including sessionization, user-day aggregation, and detection of sustained behavioral patterns in event data; it is categorized under Data Manipulation (SQL/Python) and targets practical application skills.
Data Manipulation (SQL/Python)Onsite - 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
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