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
Easy
Data Scientist Locked

Compute Bayes probability for fake accounts

This question evaluates Bayesian reasoning and probabilistic modeling skills, including conditional probability, base-rate effects, detector character...

Statistics & Math
14
1
104 people solved
Nov 1, 2025
Meta logo
Meta
Medium
Data Scientist Locked

Write SQL for multi-account metrics

This question evaluates proficiency in SQL for multi-table aggregation, grouping, joins, and conditional counting within a user-account-notification s...

Data Manipulation (SQL/Python)
7
1
51 people solved
Mar 16, 2026
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Meta
Medium
Data Scientist Locked

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...

Analytics & Experimentation
4
1
68 people solved
Mar 16, 2026
Meta logo
Meta
Medium
Data Scientist

Diagnosing a drop in total ads revenue

Diagnosing a Sharp Drop in Global Ads Revenue You are a data scientist supporting a large ads marketplace. Last week, global ads revenue declined shar...

Analytics & Experimentation
34
1
86 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data ScientistNew Grad

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...

Behavioral & Leadership
94
0
237 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Develop a Restaurant-Recommendation Engine with Logistic Regression

Develop a Restaurant Recommendation Engine with Logistic Regression You are designing a restaurant recommendation engine for a social app. You need to...

Machine Learning
108
0
333 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Identify User Interest in Group Video Calls Using Data

Identify User Interest in Group Video Calls Using Data You are designing and analyzing a new group video-calling feature for a large social or messagi...

Analytics & Experimentation
174
3
302 people solved
Jul 12, 2025
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Meta
Medium
Machine Learning Engineer Locked

Solve array merge, tree view, and maze tasks

This question evaluates array manipulation and in-place algorithms, binary tree traversal and visibility reasoning, and grid graph traversal for path ...

Coding & Algorithms
3
0
38 people solved
Dec 9, 2025
Meta logo
Meta
Medium
Software Engineer

Describe resolving conflict by persuading others

Describe a time you had a significant work-related conflict or disagreement with a teammate, stakeholder, or manager where you believed your approach ...

Behavioral & Leadership
2
0
53 people solved
Dec 8, 2025
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Meta
Medium
Software Engineer

Design ticketing system with seat hold

Design an online ticketing system (similar to Ticketmaster) that allows users to browse events, select seats, and place a temporary 5-minute hold on s...

System Design
4
0
58 people solved
Dec 8, 2025
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Meta
Medium
Software Engineer

Describe an ambiguous project you handled

Describe a time when you worked on a project with a lot of ambiguity (unclear requirements, unknown constraints, or shifting priorities). Explain: - W...

Behavioral & Leadership
2
0
48 people solved
Dec 8, 2025
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Meta
Medium
Software Engineer

Design an online coding platform

Design an online coding practice and interview platform (similar to well-known coding challenge sites). The platform should allow: - Users to sign up,...

System Design
4
0
43 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Prepare answers for core behavioral questions

Behavioral Questions Set You are preparing for a behavioral interview. Be ready to answer the following questions clearly and concretely, ideally usin...

Behavioral & Leadership
3
0
32 people solved
Dec 7, 2025
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Meta
Medium
Data Scientist

Assess ranking change and design experiment

A multi-account product currently orders a user's accounts by most recent visit. The product team wants to change the ranking so that accounts with th...

Analytics & Experimentation
3
0
46 people solved
Jan 21, 2026
Meta logo
Meta
Medium
Data Scientist

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...

Machine Learning
3
0
31 people solved
Aug 10, 2025
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Meta
Medium
Data Scientist Locked

Write SQL for CTR and Revenue

This question evaluates proficiency in SQL-based data manipulation and analytics, focusing on joins across event and reference tables, time-of-day buc...

Data Manipulation (SQL/Python)
13
1
91 people solved
Mar 12, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design a fitness tracking app

This question evaluates a candidate's competency in designing end-to-end backend architectures, specifying core APIs, modeling workout and geospatial ...

System Design
7
0
61 people solved
Mar 12, 2026
Meta logo
Meta
Medium
Software Engineer

Discuss project pride, conflict, and pivots

Discuss project pride, conflict, and pivots Behavioral & Leadership (Technical Screen) Context You are a software engineer in a technical screen that ...

Behavioral & Leadership
2
0
43 people solved
Aug 7, 2025
Meta logo
Meta
Hard
Software Engineer

Solve common array/string/linked-list tasks

You may be asked one or more of the following independent coding tasks. For each task, implement an efficient algorithm and clearly state time/space c...

Coding & Algorithms
5
0
46 people solved
Oct 21, 2025
Meta logo
Meta
Medium
Data Scientist

Construct a 95% Confidence Interval for Comment Counts

Construct a 95% Confidence Interval for Comment Counts Comment Activity Analysis: Mean CI, Sampling Distribution, and 95th Percentile Context You have...

Statistics & Math
3
0
42 people solved
Aug 4, 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.

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