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
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Meta
Medium
Data Scientist

Evaluate Probability of Positive User Comments and Model Performance

Evaluate Probability of Positive User Comments and Model Performance Social-Media Positivity: Independence and Model Comparison Context You are evalua...

Statistics & Math
107
0
381 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Determine Success Metrics for Instagram Video-Call Feature

Determine Success Metrics for Instagram Video-Call Feature Instagram Group Video-Call MVP: Defining Success and Metrics Context You are the data scien...

Analytics & Experimentation
3
0
27 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Explain Type I vs. Type II Errors in A/B Testing

Explain Type I vs. Type II Errors in A/B Testing A/B Testing Errors and Estimation Under Skewed Metrics Context You are analyzing an A/B experiment fo...

Statistics & Math
20
0
56 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Design Sampling Strategy to Estimate Fake News Proportion

Design Sampling Strategy to Estimate Fake News Proportion Estimating Fake News Prevalence and Impact on Facebook Context Management is concerned about...

Statistics & Math
24
0
31 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Boost Engagement and Purchases in Meta Social Products

Boost Engagement and Purchases in Meta Social Products Meta Social Products: Driving Comments in Facebook Groups and In‑App Purchases on Instagram Con...

Analytics & Experimentation
2
0
30 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Convince Leadership to Launch Group Chat Feature

Convince Leadership to Launch Group Chat Feature Evaluating a Group Chat / Group Video-Call Feature for Instagram Context You are a Data Scientist ask...

Analytics & Experimentation
6
0
41 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Calculate and Compare Survey Response Rates for User Tenure

Surveys +--------+------------+--------------+----------+ | userid | date | survey_event | response | +--------+------------+--------------+----...

Data Manipulation (SQL/Python)
1
0
12 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes A/B Test: Heterogeneous Lift in CTR for a New Ad-Ranking Algorithm Context You ...

Analytics & Experimentation
64
0
165 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Design A/B Test to Evaluate New Video-Feed Feature

Design A/B Test to Evaluate New Video-Feed Feature Scenario A consumer social-media app is launching a short‑video feed (TikTok-style). A newly added ...

Analytics & Experimentation
2
0
27 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Determine Value of Prioritizing Accounts by Unread Notifications

Determine Value of Prioritizing Accounts by Unread Notifications Feature Validation: Ordering Multiple Accounts by Unread Notifications Context Users ...

Analytics & Experimentation
5
0
57 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Determine Key Metrics and Design A/B Test for Ad Ranking

Determine Key Metrics and Design A/B Test for Ad Ranking Experiment Design: Replacing Rule-Based Ad Ranking with a Recommender Context You are launchi...

Analytics & Experimentation
5
0
43 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Detect and Reduce Spammy Friend Requests Effectively

Detect and Reduce Spammy Friend Requests Effectively Detecting Spammy Friend Requests Context Assume a consumer social platform where users can send f...

Machine Learning
2
0
26 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Discuss conflicts, deadlines, and persuasion

Discuss conflicts, deadlines, and persuasion Behavioral and Leadership Prompts for a Data Engineer (Onsite) Context: You are interviewing for a Data E...

Behavioral & Leadership
2
0
44 people solved
Aug 1, 2025
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Meta
Hard
Software Engineer

Design searchable status & geolocation services

Design searchable status & geolocation services System Design: Searchable Short-Form Status Updates with Nearby Search Context Design a microblogging ...

System Design
7
0
49 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Handle feedback and priority conflicts

Handle feedback and priority conflicts Behavioral & Leadership Interview (Software Engineer, Onsite) Context: You will be asked to demonstrate ownersh...

Behavioral & Leadership
24
0
62 people solved
Jul 29, 2025
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Meta
Hard
Software Engineer

Design coding platform leaderboard system

Design coding platform leaderboard system System Design: Scalable Coding Platform with Live Global Leaderboard Context Design a coding challenge platf...

System Design
15
0
83 people solved
Jul 29, 2025
Meta logo
Meta
Medium
Software Engineer

Implement right side view and local minimum search

Question LeetCode 199. Binary Tree Right Side View — Given the root of a binary tree, return the values of the nodes you can see ordered from top to b...

Coding & Algorithms
29
0
79 people solved
Jul 29, 2025
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Meta
Hard
Data Scientist Locked

Design an A/B test for non-friend posts

Design an A/B test for non-friend posts evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommen...

Analytics & Experimentation
1
0
27 people solved
Jul 28, 2025
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Meta
Hard
Data Scientist Locked

Measure whether posts strengthen friendships

Measure whether posts strengthen friendships evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and rec...

Analytics & Experimentation
2
0
27 people solved
Jul 28, 2025
Meta logo
Meta
Medium
Software Engineer

Find minimum and most frequent number efficiently

Find minimum and most frequent number efficiently Given an unsorted integer array, write functions to: (a) find the minimum value; and (b) find any va...

Coding & Algorithms
2
0
22 people solved
Jul 27, 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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