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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Describe resolving conflict and welcoming others
Answer the following behavioral questions with specific examples: 1. How do you make other people feel welcome or included on a team? - Especially ...
Design measurement to detect fake accounts
Context You work on a social platform. The only product surface you can rely on is friend requests (sending/receiving/accepting/declining). Assume you...
Design a Coding Contest Platform
Design an online coding contest platform that supports programming competitions, code submission, automated judging, and live leaderboards. The platfo...
Explain your main growth area
What is one of your main growth or development areas right now? Explain: - What specific skill or behavior you are working to improve. - How you ident...
Design an A/B test for a new shop-ads algorithm
A new ranking/promotion algorithm will change which shop ads are shown (and their order). You are asked: “How do we know if this new algo is good?” De...
Compare Bayesian and frequentist decisions
A/B Test With Beta–Binomial Posteriors and Decision-Making Under Asymmetric Costs You ran a two-arm A/B test on a binary KPI with independent Beta(1, ...
Choose alternatives when randomization fails
Causal Impact of an Autoloaded Feature Without Clean Randomization Context You need to estimate the causal effect of a new autoloaded feature that is ...
Design robust group size limiting for calls
Design the admission-control and enforcement algorithm to limit group-call size under real-world race conditions. Constraints: multiple SFU edges in m...
Choose and compute recommender evaluation metrics
Restaurant Recommender: Offline Evaluation and Modeling Context: You are scoring p(y=1|x) with logistic regression to predict if a user will engage wi...
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...
Design a restaurant recommender under constraints
This question evaluates a candidate's competency in designing scalable machine learning recommender systems, covering retrieval and ranking architectu...
Estimate CTR lift with binomial tests and errors
A/B Test Inference, Peeking, and Multiple Comparisons You run a two-arm A/B test of click-through rate (CTR). - Control: n_c = 10,000,000 impressions,...
Lead a product deep dive with quantified impact
Behavioral Product Leadership Prompt (Data Scientist) You are interviewing for a Data Scientist role with a strong focus on product analytics, experim...
Design a ranking and recommendation system
This question evaluates a candidate's competency in designing end-to-end ranking and recommendation systems, covering candidate generation, ranking, f...
Describe failures, self-reflection, and conflict resolution
Answer the following behavioral prompts with recent examples: 1. Self-reflection / improvement - “In a recent project, where could you have done be...
Design an online auction platform
Design an Online Auction System Design a scalable, highly available online auction platform where users can list items for auction and other users can...
Describe a challenging project and work-style conflicts
Question This is the Meta Data Scientist onsite behavioral & leadership round. The anchor prompt is to describe your most challenging recent project e...
Describe cross-team collaboration and learning from failure
Answer the following behavioral prompts using specific examples from your experience: 1. Cross-team collaboration: Tell me about a project where you w...
Design nearby place recommendations
Real‑Time Nearby Places Recommendation System Context Design a mobile feature that recommends nearby places (e.g., restaurants, shops, attractions) to...
Design a price tracking system
Question Design a price tracking system for e-commerce sites (similar to price-history tools such as CamelCamelCamel or Keepa). The system ingests pro...