Meta Data Manipulation (SQL/Python) Interview Questions
Practice 1,159 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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Resolve a team conflict decisively
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Design a clustered notification experiment with guardrails
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Reflect on feedback and metric trade-offs
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Define composite success for search and test it
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Design B2C chatbot success metrics and test plan
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Design an A/B test for WhatsApp call reliability
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Design a small-sample launch experiment in Europe
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Define success metrics and guardrails for B2B chat
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Choose robust metrics for skewed comments
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Analyze regression to mean in heavy-tailed shares
Cohort Dynamics of a Right-Skewed Daily Shares Metric Context - You have a right-skewed metric: daily shares per user, with a long tail. - On Day 1, y...
Compute conditional occupancy across two rooms
Probability and Bayes Update: Two Rooms Setup There are two rooms. Prior over occupancy states: - With probability 1/3: both rooms are occupied. - Wit...
Derive expected meetings given nonempty room
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Compare two ad insertion strategies
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Model user-level ad impression allocation
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