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

Calculate Posterior Probability of Flagged User Being Bad Actor

Calculate Posterior Probability of a Flagged User Being a Bad Actor A platform runs a binary classifier that flags users who might be bad actors. You ...

Statistics & Math
107
1
272 people solved
Jul 12, 2025
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Meta
Easy
Data Scientist

Compare Ad-Insertion Strategies: Expected Ads and Probabilities

Newsfeed Ad-Insertion Strategies You are evaluating two ways to insert ads into a user's newsfeed. A user views a contiguous sequence of posts. - Stra...

Statistics & Math
37
0
133 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Measuring and mitigating fake news on Facebook

Measuring and Mitigating Fake News Under Reviewer Constraints Policy teams need an overnight view of fake-news prevalence on the platform, but only a ...

Analytics & Experimentation
74
0
144 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Metrics for Restaurant-Feature Impact and Engagement Trade-offs

Evaluate Metrics for Restaurant-Feature Impact and Engagement Trade-offs A large social app launches a restaurant-recommendation feed that may compete...

Analytics & Experimentation
86
0
183 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Choose Metrics for Evaluating Fake-User Classifier

Choose Metrics for Evaluating a Fake-User Classifier A sudden spike in daily average comments may be driven by fake users. You are asked to build a bi...

Machine Learning
19
0
49 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Success of B2C Chat App with Key Metrics

Evaluate Success of a B2C Chat App with Key Metrics A data scientist is asked to define how to evaluate the overall success of a business-to-consumer ...

Analytics & Experimentation
5
0
28 people solved
Jul 12, 2025
Meta logo
Meta
Easy
Data Scientist

Determine Posterior Probability of Bad User Prediction

Posterior Probability for a Bad-Actor Classifier You are evaluating a binary classifier that flags bad actors among users. Given: - 5% of users are tr...

Statistics & Math
33
0
134 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Key Statistics for Article Comment Distribution Analysis

Analyze Comment Counts per Article You are analyzing the distribution of the number of comments each article receives on a content website. You have c...

Statistics & Math
86
1
183 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Design Experiment to Measure Shopping Feature Impact

Experiment Design: Measure Instagram Shopping Impact Instagram is launching an in-app Shopping feature, such as product tags, shop surfaces, or in-app...

Analytics & Experimentation
10
0
57 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Build Trust Quickly with New Team Stakeholders

Build Trust Quickly with New Team Stakeholders This behavioral prompt assesses cross-functional collaboration for a data scientist role. The interview...

Behavioral & Leadership
17
0
81 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Metrics for Group-Video Calling Experiment Success

Determine Metrics for Group-Video Calling Experiment Success You are the data scientist for a large consumer messaging app that currently supports one...

Analytics & Experimentation
83
0
293 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Evaluating the Facebook ‘Memory’ feature

Evaluating the Facebook Memories Feature You are asked to assess whether the Memories feature, which resurfaces users' past posts, delivers real user ...

Analytics & Experimentation
95
0
294 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Assessing whether a new metric A is meaningful for News Feed

Evaluating a Proposed Proxy Metric for News Feed A partner team proposes metric A as a proxy for "meaningful interactions" in News Feed. Before adopti...

Analytics & Experimentation
28
0
88 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Messenger's P2P Payments Feature for Business Viability

Evaluate Messenger's P2P Payments Feature for Business Viability Facebook Messenger is considering launching a Venmo-like peer-to-peer money transfer ...

Analytics & Experimentation
85
0
251 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Interpreting metrics when autoplay videos reduce time‑spent but increase DAU

Autoplay Snippets: Time Spent Down, DAU Up You are analyzing an A/B test where short autoplay video previews were enabled in feed. Per-session time sp...

Analytics & Experimentation
13
0
45 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Recommendation Feature with Historical Data Analysis

Offline Evaluation of a Recommendation Feature With Historical Data The company is considering launching a new recommendation-system feature and wants...

Analytics & Experimentation
24
0
49 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Ad Insertion Statistics for Two Methods

Ad Insertion Statistics for Two Feed Strategies You are comparing two ways of inserting ads into a 100-post feed. - Option A: Each post independently ...

Statistics & Math
73
0
78 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Design Experiment to Evaluate New Video-Ad Effectiveness

Design an Experiment to Evaluate New Video-Ad Effectiveness A large consumer app is considering a new video-ad format with changes to UI, creative ren...

Analytics & Experimentation
83
0
42 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Classify Reviewers Using Bayesian Probability for Accuracy Analysis

Classify Reviewers With Bayesian Probability You are auditing reviewers who may be lazy or careful. Each reviewer completes n gold-standard review tas...

Machine Learning
92
0
253 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Analyzing abuse in the content‑reporting system

Measuring Valid Reports and Detecting Abuse in a Reporting System Analyze a user reporting system over a 30-day window. The schema is: reports(report_...

Analytics & Experimentation
12
0
35 people solved
Jul 12, 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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