Meta 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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"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
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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."
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Design versioned in-memory key-value store
This question evaluates understanding of in-memory data structures, versioning semantics, rollback mechanisms, and performance trade-offs between time...
Implement list cloning and k-frequency finder
You are given two separate coding tasks. --- Problem 1: Deep copy a linked list with extra pointers You are given the head of a singly linked list. Ea...
How investigate a brand-ad spend drop?
Meta has a video ads product with two ad types: - Direct ads: optimized for in-platform actions - Brand ads: users click the video ad and are sent to ...
How would you predict a car’s turning intention?
At an intersection, there are n vehicles stopped or approaching. For each vehicle, you have a short history (e.g., last 3–10 seconds at 10 Hz) of: - P...
How would you validate a driving simulator’s realism?
You work on autonomous driving evaluation. You have two datasets for the same set of driving scenarios: - Real-world logs collected from vehicles (gro...
Implement an in-memory key-field-value DB with TTL
In-Memory DB with TTL + Scan + Backup/Restore Implement an in-memory database storing records by (key, field) -> value with optional TTL. Data model /...
Describe Overcoming a Major Challenge in Your Career
Describe Overcoming a Major Challenge in Your Career This is a behavioral deep-dive for a new-grad data scientist role. The interviewer may ask severa...
Describe Handling Cross-Functional Projects and Changing Priorities
Describe Handling Cross-Functional Projects and Changing Priorities This behavioral prompt evaluates how you collaborate across functions, respond to ...
What features and feature selection would you use?
Context You are building an ML system to rank/promote shop ads in an e-commerce feed/search page. At serving time, the system may score candidate shop...
Derive Linear Regression Solution
This question evaluates understanding of one-dimensional linear regression estimation, the statistical derivation of the mean squared error objective ...
Propose an ads recommendation model for shop ads
You need to propose a modeling approach for recommending/ranking shop ads (i.e., which shop ads to show and in what order) for a marketplace app. Desc...
Measure impact of bot mitigation via experiment
Experiment Design: Measuring the Impact of a Bot‑Mitigation System Context You are evaluating a production change to a large social platform that hide...
Build a model to infer home vs office vs public
You must infer whether a Facebook session’s network context is home, office, or public venue to inform Portal targeting. Constraints: IPs may be share...
Choose threshold under asymmetric costs
You own a credit-card fraud classifier deployed as a probability scorer. Choose an operating threshold under asymmetric costs and justify it quantitat...
Measure a friend-recommendation launch
A new friend-recommendation algorithm ships behind a feature flag. Design how you will measure success and decide whether to launch: - State no more t...
Replace legacy ads model safely
Facebook Ads Ranking Replacement: M0 to M1 You are asked to replace a legacy ads ranking model (M0) with a new model (M1) in a large-scale feed ads sy...
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...
Test two models' proportions for significance
Two search models, A and B, were each used once by 100 distinct users (one query per user). Success is defined per query by your composite metric (suc...
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...
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...