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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 2401–2430 of 4616 results
- MetaEasyProduct AnalystDesign experiments and diagnose metric changes
This question evaluates product analytics and experimentation skills, including experiment design, metric framework definition, causal reasoning, diagnostic analysis, and marketplace operations trade-offs within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaHardMachine Learning EngineerDesign image and multimodal generation systems
This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.
ML System DesignTechnical Screen - MetaHardSoftware EngineerDesign input validation and error handling
A Meta software engineer system design technical screen: design the API and system-level input validation and error-handling strategy for a service that computes the top-k most frequent elements in a posted array. The merged answer covers the request/response contract with a worked example, payload and cardinality limits, layered validation across client, gateway and service, authentication, rate limiting, idempotency, timeouts and retries, the full 4xx/5xx error taxonomy with sample error responses, partial-failure modes, graceful degradation to approximate top-k with stated error bounds, defenses against compression bombs and poisoned payloads, observability, and a test and production monitoring plan.
System DesignTechnical Screen - MetaMediumData ScientistDesign marketplace experiments at DoorDash
This question evaluates a candidate's skills in experimental design, product and marketplace analytics, causal inference, and diagnostic data science for multi-sided platforms.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign metrics for violating content exposure
This question evaluates competency in metric design and measurement for content safety, including defining exposure metrics, inclusion/exclusion rules, handling label latency and appeals, uncertainty-aware alerting, and A/B test design for a Data Scientist role in the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 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 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 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 - 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 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
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