Meta Interview Questions

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.

1.2k Questions 1 Company07.06.2026
Showing 20 results
Role
Meta logo
Meta
Medium
Data Scientist

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...

Machine Learning
5
0
42 people solved
Oct 14, 2025
Meta logo
Meta
Medium
Data Scientist

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...

Machine Learning
2
0
42 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

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...

Machine Learning
6
0
56 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Estimate delayed CVR nonparametrically with censored data

Today is 2025-09-01. We need the 14-day conversion rate (CVR14) for impressions served between 2025-08-18 and 2025-09-01, but many conversions occur w...

Statistics & Math
8
0
57 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

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...

Machine Learning
7
0
49 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Persuade engineers to launch pinned-unread chats

Pitch: Pinning Conversations for High-Unread Users Context You are proposing a feature that pins a small set of conversations to the top of the inbox ...

Behavioral & Leadership
4
0
49 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design a hashtag recommender for News Feed

Design: Hashtag Recommendations in the News Feed Context You are adding hashtag recommendations alongside posts in a large social app’s News Feed. The...

Machine Learning
5
0
42 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

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...

Analytics & Experimentation
6
0
49 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Clarify scope and align to mission

Clarify and Align: New Google Maps Feature to Boost Group Page Engagement Context (Completed) Assume "Group pages" are shared spaces in Google Maps wh...

Behavioral & Leadership
6
0
47 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Prove source growth is cannibalization, not incremental

Causal Analysis Design: Is Web Growth Incremental or Cannibalization? Background You observe that revenue attributed to creation_source = "web" is hig...

Analytics & Experimentation
3
0
36 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Model session times and comments with exponential/Poisson

Session Duration Memoryless Assumption and Poisson Comment Counts Setup - We model user session end times with a constant hazard (memoryless) over tim...

Statistics & Math
2
0
28 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate fraud classifier with cost-sensitive metrics

Binary Fraud Classifier: Metrics, Thresholding, Calibration, and Online Evaluation You inherit a binary fraud classifier used to decide whether to blo...

Machine Learning
3
0
31 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

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...

Statistics & Math
2
0
41 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate shopping tab pre- and post-launch

Instagram Shopping Tab — Measuring Off‑App Purchases, Opportunity Sizing, and Launch Readout Context Instagram is planning a new Shopping tab. Users o...

Analytics & Experimentation
5
0
45 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Compute fraud probabilities with Bayes and Binomial

Fake-Account Detection with Binomial Sessions and Bayes Updating You are evaluating a rules-based detector for fake accounts on an online platform. Ea...

Statistics & Math
10
2
90 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Software Engineer

Solve four algorithmic problems

Solve the following coding tasks. For each, describe your approach, complexity, and handle edge cases. 1) Make parentheses string valid with minimal d...

Coding & Algorithms
6
0
51 people solved
Oct 12, 2025
Meta logo
Meta
Hard
Software Engineer

Merge customers and preserve history

Question Implement mergeCustomers(String oldId, String newId) for a payments/ledger platform. The call absorbs oldId into the survivor newId while pre...

System Design
6
0
48 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Software Engineer

Describe conflict resolution and leadership

In a senior software engineering phone screen, you may be asked several behavioral questions around collaboration, conflict management, and leadership...

Behavioral & Leadership
2
0
44 people solved
Feb 1, 2026
Meta logo
Meta
Hard
Software Engineer

Design a bank system with scheduling and rankings

Design a bank system with scheduling and rankings Design an In‑Memory Bank System (Technical Screen) You are designing an in‑memory bank ledger that s...

System Design
6
0
96 people solved
Jul 27, 2025
Meta logo
Meta
Medium
Software Engineer

Answer conflict, ambiguity, feedback, difficult coworker prompts

Prepare behavioral answers for these prompts: 1) Describe a time you had a conflict with your manager. How did you resolve it? 2) Describe a project w...

Behavioral & Leadership
3
0
44 people solved
Oct 2, 2025

Frequently Asked Questions

How difficult are Meta interview questions?
Meta interview questions span a wide difficulty range because they must screen candidates from entry to senior levels across many functions. Expect coding rounds to map to medium-to-hard algorithmic problems that appear in top 100 problem lists for software engineers, and expect data roles to face challenging SQL, experiment diagnosis, and product-analytics problems that require clean metric definitions. Machine learning roles emphasize recommendation and ranking tradeoffs and model complexity, while data engineers encounter large-scale ETL and modeling puzzles. Difficulty scales with level: entry hires see clearer, bounded problems; senior hires face ambiguous tradeoffs and system-wide thinking.
What is Meta's interview process and where do these questions appear?
Meta typically runs a multi-stage process: recruiter screen, one or two technical screens or an online assessment, a full loop of onsite-style interviews, then debrief, committee review, and offer. The full loop mixes coding, system or product design, role-specific technical rounds, and behavioral interviews. Software-engineer candidates spend most time on coding and design; data scientists focus on SQL, experimentation, and product analytics; machine-learning engineers see modeling and recommendation design; data engineers handle SQL, data modeling, and pipeline questions; PMs get product-design and analytics probes. In 2025–2026 some teams pilot AI-enabled coding rounds.
How should I structure a preparation timeline for a Meta interview?
A focused six-week plan works well: weeks one and two cover fundamentals—data structures, algorithms, SQL basics, and experiment design; weeks three and four emphasize timed problem practice, mock phone screens, and role-specific cases (A/B diagnosis for data scientists, model design for MLEs, ETL modeling for data engineers); week five concentrates on system or product design and behavioral storytelling; week six is for full mock loops, timing, and refining communication. Practice with realistic tools, simulate loop pacing, and schedule a debrief after each mock to iterate on clarity, edge-case handling, and time management.
Which technical subtopics are most commonly tested for each role at Meta?
For Data Scientist interviews the recurring technical themes are product-metric definition, diagnosing experiment and spend drops, counting multi-account interactions, SQL for multi-entity metrics, and ranking or recommendation evaluation such as shop ad ranking. Software-engineer questions frequently focus on timestamped state and versioned systems, leaderboards and ranking, maze/graph traversal and tree/array transforms, delimiter and CSV parsing, and scalable search or flight-search style designs. Machine-learning engineers see place and friend recommendation design, sparse-matrix operations, ranking/loss choices, and feed dislike or personalization models. Data engineers repeatedly face entity modeling for feed and booking data, SQL analytics for utilization and reservations, and capacity-aware aggregation challenges.
What standout tips and common pitfalls should I watch for in Meta interviews?
Start interviews by clarifying requirements and expected outputs, then propose measurable success metrics; this prevents misaligned solutions. For coding, think aloud, handle edge cases, state complexity up front, and write a couple of quick tests. In design rounds quantify load, storage, and tradeoffs rather than vague features. Data roles must define metrics, guardrails, and experiment assumptions before jumping to analysis; common pitfalls are ambiguous metric definitions, peeking at tests, and ignoring instrumentation limits. For AI-assisted coding rounds, use the assistant to accelerate boilerplate but validate logic and corner cases yourself. Finish each answer with a concise summary of impact and tradeoffs.

Explore more Meta interview questions

Jump straight to Meta questions for a specific role or category.

By role
By category
In-depth guides
Across all companies

Featured Meta interview prep guides

Concept walkthroughs, worked examples, and the real questions from candidate reports.

Editorial prep
Software Engineer
Meta interview
Read the guide
Editorial prep
Data Scientist
Meta interview
Read the guide