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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"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
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Choose robust metrics for skewed comments
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Build a Bayes classifier for reviewer types
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Compute posterior for accurate-but-rare classifier
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Derive expected meetings given nonempty room
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Compare two ad insertion strategies
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Derive no-click probability and sketch implications
Click Probability Across Repeated Impressions Context: We show A impressions of the same item to a user. Unless otherwise stated, each impression is a...
Measure notification impact and set guardrails
This question evaluates causal inference, experiment design, metric specification and attribution, statistical power calculation, and long-term monito...
Identify latent group-call demand from behavior
This question evaluates a data scientist's ability to design measurable product-analytics signals, infer latent user demand from event-level messaging...
Estimate and validate weights for engagement actions
This question evaluates statistical modeling and inference skills including constrained weighting, uncertainty quantification, multicollinearity and s...
Design experiment for unconnected content in feed
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Write SQL for social feed metrics and ties
You are given the following schema (PostgreSQL) and sample rows. Assume UTC timestamps and that friendships are static over the sample window. users(u...
Resolve cross-team conflict and align incentives
Behavioral & Leadership: Cross-Team Conflict With Tight Timeline You are a Data Scientist interviewing for an onsite role. Describe a realistic cross-...
Design and justify unread-accounts pinning experiment
Experiment Design: Pin Unread Accounts at Top of Account Switcher Context You propose a feature for users who own multiple accounts (same person_id): ...
Compute multi-account activity and unread percentages in SQL
You are given two tables. Use them as the source of truth and do not assume any other data. Table: notifications +--------+------------+------------+-...
Design metrics and geo A/B for new feature
Marketplace Experiment: Verified Seller Badges Context: You are evaluating a new Marketplace feature, Verified Seller Badges, designed to improve buye...
Resolve exclusion, learn fast, and manage conflict
Behavioral & Leadership Onsite — Cross-Team Inclusion, Fast Learning, Analytical Conflict Context You are a data scientist working cross-functionally ...
Design and justify unread-account pinning experiment
This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, instrumentation, and analysis for p...
Estimate fake-account prevalence with capture-recapture
This question evaluates a data scientist's competency in capture–recapture estimation, estimation of population size with incomplete detections, stati...
Design a feed ads A/B test with guardrails
Experiment Design: Insert One Extra Ad Every 8 Organic Posts in Main Feed Context You want to increase ad load by inserting one additional ad for ever...
Design Messenger spam experiment with clustering
Experiment Design: Spam-Detection Algorithm for Messenger You are evaluating a new spam-detection algorithm that routes suspected spam into a separate...