Meta Behavioral & Leadership 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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"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
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"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."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Implement a versioned in-memory DB with CAS and history
In-Memory DB V2: TTL + Compare-And-Set/Delete + Historical Reads Implement an in-memory database keyed by (key, field) supporting TTL, conditional upd...
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, ...
Apply sequential testing without p-hacking
Sequential Monitoring With Early Stopping Context: You are planning a two‑sided hypothesis test with continuous monitoring and early stopping for effi...
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 and experiment on Group Call feature
Assume today is 2025-09-01. You have only one table, calls_daily_agg(date, user_id, country, device_tier, one_to_one_calls_started, one_to_one_call_du...
Increase posts receiving comments via experimentation
Increase the Share of Posts That Receive a Meaningful Comment You are a data scientist for a consumer social app with posts and comments. Your goal is...
Tune fraud threshold under review capacity and costs
Fraud Triage Thresholding with Calibrated Scores Context You have a fraud model that outputs a calibrated score s ∈ [0, 1] per account, where s ≈ P(fa...
Compute posterior and event counts in fraud screen
Fake-Account Screening with Threshold on 5 Signals You are designing a rule-based screener that flags an account if at least k of 5 binary signals fir...
Explain Your Motivation for Career Transition and Role Interest
Explain Your Motivation for Career Transition and Role Interest Behavioral Phone Screen — Data Scientist Context You’re in a recruiter/technical phone...
Evaluate and Experiment with Harmful Content Detection Model
Evaluate and Experiment with Harmful Content Detection Model Evaluating a Harmful-Content Detection Model: Offline and Online Context You are given a ...
Schedule and cancel delayed payments
Question Extend an existing in-memory payment system (immediate transfers between accounts, plus a top-N spenders/payers leaderboard) with scheduled p...
Design tables for event-driven metrics
Design a Relational Schema for Consumer-App Event Analytics Context and Assumptions You are designing the event store for a high-volume consumer app. ...
Compute Each Advertiser's Share of Shop Ad Spend
Compute Each Advertiser's Share of Shop Ad Spend You have the following daily advertising table: `text ads_detail( advertiser_id, ad_id, ad_type...
Which clustering algorithm would you use and why
Question You need to cluster users for a social product (e.g. Meta) to discover meaningful groups such as communities, interest groups, or usage segme...
Simulate Monster Team Battles
This question evaluates a candidate's ability to model stateful simulations and implement deterministic battle mechanics with clean data structures an...
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...
How to test account ranking change
This question evaluates a data scientist's competency in causal inference, experimentation design, metrics selection, and observational analysis using...
Evaluate WhatsApp Group Video Calling
Meta is considering improvements to WhatsApp group video calling. The product team wants to understand whether users need this feature, how to increas...
Design an ads ranking system with calibration
This question evaluates a candidate's ability to design scalable, low-latency online machine learning systems for ads ranking, covering competencies i...