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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
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- 11
- Topics
- 8
- Languages
Showing 5041–5070 of 9403 results
- 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 - MetaMediumData ScientistDesign pre-launch plan and cluster A/B test
This question evaluates a candidate's skills in experimental design, causal inference, cluster randomization, metric definition and instrumentation, power analysis, and phased rollout decision-making.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerDesign prefix-sum function and max stack
This question evaluates understanding of prefix-sum precomputation for constant-time range queries and the design of a max-enabled stack, focusing on data structure design, algorithmic complexity, and space-time trade-offs.
Coding & AlgorithmsTechnical Screen - 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 - MetaMediumData ScientistDesign robust group size limiting for calls
This question evaluates distributed systems design, concurrency control, admission-control algorithms, and scalability under real-time and race-condition constraints.
Coding & AlgorithmsTechnical Screen - 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 - MetaHardData EngineerDesign tables for event-driven metrics
This question evaluates relational data modeling and data engineering competencies for high-volume event analytics, including schema design, partitioning and clustering strategies, duplicate and late-arriving event handling, and normalization versus denormalization trade-offs.
System DesignTechnical Screen - 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 - MetaMediumData ScientistDesign video-ads experiment and handle null results
This question evaluates experimental design, causal inference, statistical power analysis, metric definition, and operational analytics competency within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - 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 - MetaMediumSoftware EngineerDetect a Contiguous Subarray with a Target Sum
Determine whether an integer array containing negative values has any nonempty contiguous subarray that equals a target, using prefix sums for a linear-time solution.
Coding & AlgorithmsTechnical Screen - 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 - MetaEasyData ScientistDetect bots using comment distribution patterns
This problem tests your ability to define metrics for identifying automated behavior and your statistical intuition for validating those metrics against human behavior.
Analytics & ExperimentationTechnical ScreenAnalyticsAnomaly DetectionFeature Engineering - 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 - MetaMediumData ScientistDetermine High-Quality Notifications with CTR Analysis
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 High-Quality Notifications with CTR Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - 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 - MetaMediumData ScientistDetermine Value of Prioritizing Accounts by Unread Notifications
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 Value of Prioritizing Accounts by Unread Notifications states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerDetermine feasibility and clean parentheses string
This problem tests your ability to manipulate string structures using stack-based logic and perform graph-based dependency resolution using topological sort.
Coding & AlgorithmsTechnical ScreenStringsStackGraphs - MetaMediumSoftware EngineerDetermine if subarray sums to target
This question evaluates a candidate's ability to reason about contiguous subarray sums, efficient array manipulation, and handling large numeric ranges within algorithmic constraints, and it falls under the Coding & Algorithms domain as a practical algorithmic implementation problem.
Coding & AlgorithmsTechnical Screen
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