Meta Behavioral & Leadership Interview Questions
Practice 1,166 real Meta interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Data Manipulation (SQL/Python), Behavioral & Leadership, and System Design — across Software Engineer, Data Scientist, Machine Learning Engineer, Data Engineer, and Product Manager roles. Real questions from actual interviews with detailed solutions. Expect a software-engineering-heavy loop: timed algorithmic coding (trees, arrays, graph/maze problems, delimiter/CSV parsing), system-design prompts like leaderboards, flight search and online-judge architectures, and an increasingly common AI-assisted coding round that mirrors real workflows. Data Scientist rounds emphasize product analytics and experimentation—designing tests, diagnosing spend drops and bots, evaluating unconnected content, and writing SQL for multi-account, seller, and vehicle metrics. Machine Learning Engineer questions skew toward recommender and ranking work (place and friend recommendation, sparse-matrix ops, linear-regression derivations, newsfeed dislike models). Data Engineers focus on data modeling, ETL, capacity calculations, reservations/utilization queries, and production SQL/Python tasks. For interview preparation, prioritize timed coding practice, system-design templates, rigorous SQL drills (joins/CTEs/aggregation), clear A/B-testing frameworks, and concise STAR behavioral stories tied to measurable impact.
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Explain background, team structure, and role fit
Answer the following in order: 1) Give a crisp 90‑second self‑introduction tailored to this role, emphasizing 1–2 quantifiable achievements most relev...
Compute CTR overall and by campaign type
Write SQL to compute: (Q1) overall click-through rate (CTR = clicks/impressions) in the last week; (Q2) CTR by campaign_type in the last week. Assume ...
Identify non-table data for feature demand
Evaluate Demand for a New "Group Call" Feature Using Non-Table Data and Experiments Context You are a data scientist evaluating whether to invest in a...
Characterize and compare transfer-count distributions over time
P2P Transfer Counts in First 30 Days: Distribution, Summaries, and Evolution Context: For a new user cohort, define X as each user’s number of peer-to...
Design a clustered notification experiment with guardrails
You work on a mobile travel app (think TripAdvisor-like) that will test a new push-notification policy recommending nearby attractions. Design a rigor...
Decide when CTR falls but revenue rises
Ads-Ranking A/B Test: Decision, Decomposition, Diagnostics, and Exec Readout Context You ran a user-level A/B test of a new ads-ranking model. The tre...
Choose KPIs for short-video recommendations
This question evaluates a data scientist's ability to define precise product metrics, set guardrails, design and power A/B tests, and apply weighted d...
Detect leakage and evaluate a prediction model
Churn Prediction Model: Leakage, Validation, KPIs, Interpretation, Monitoring Context: You inherit a weekly-scored model that predicts whether a user ...
Design and analyze an A/B test
Experiment Design: Proximity-Weighted Search Ranking A/B Test You are designing a 14-day, 50/50 user-level randomized A/B test for a marketplace's sea...
Reflect on feedback and metric trade-offs
Describe a time you chose a simpler metric under tight time constraints and later received critical feedback that it was oversimplified (e.g., from a ...
Design B2C chatbot success metrics and test plan
You own 'euro-chat', a B2C customer-support chatbot that aims to deflect agent contacts while preserving customer satisfaction. Design a rigorous succ...
Persuade engineers to launch pinned-unread chats
Pitch: Pinning Conversations for High-Unread Users Context You are proposing a feature that pins a small set of conversations to the top of the inbox ...
Design a hashtag recommender for News Feed
Design: Hashtag Recommendations in the News Feed Context You are adding hashtag recommendations alongside posts in a large social app’s News Feed. The...
Size opportunity and prioritize experiments
New E‑commerce Product Line: Pre‑Investment Quantification and Test Plan You are evaluating whether to invest engineering and operational resources to...
Choose ML metrics under asymmetric costs
This question evaluates a data scientist's competency in cost-sensitive binary classification, covering skills such as defining business cost matrices...
Model preference without ground truth
This question evaluates a data scientist's competency in uplift modeling, causal inference, experimental design, weak supervision, and bias and shift ...
Build predictive model for feature rollout targeting
Before global launch, you want to predict which users or products would benefit most from the 'More like this' button so you can stage rollout. Design...
Build dashboard; diagnose engagement–purchase gap
Build a Comprehensive Dashboard for the Shopping Tab (Organic Only) Context Assume the Shopping tab is an in-app surface for organic product discovery...
Select interest thresholds under skewness and cost
Profit-Optimal Threshold Selection from an Interest Score You have a per-user interest_score s ∈ [0, 1] for a new feature. The score distribution appe...
Prove source growth is cannibalization, not incremental
Causal Analysis Design: Is Web Growth Incremental or Cannibalization? Background You observe that revenue attributed to creation_source = "web" is hig...