Meta Interview Questions

Meta Behavioral & Leadership Interview Questions

Practice 1,166 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 Company08.08.2026
Showing 20 results
Role
Meta logo
Meta
Hard
Data Scientist

Determine User Demand for New Video-Calling Feature

Determine User Demand for New Video-Calling Feature Analytics Design: Demand Sizing, Evaluation Metrics, and Trade-offs for a New Video-Calling Featur...

Analytics & Experimentation
4
0
31 people solved
Aug 4, 2025
Meta logo
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
Meta logo
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
Meta logo
Meta
Hard
Data Scientist

Determine User Need for In-App Video Call Feature

Determine User Need for In-App Video Call Feature Scenario A consumer messaging app is considering launching an in-app Video Call feature. You have ac...

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

Analyze Social Media Engagement with SQL Queries

info_stream_views +----------+---------+-----------+-------------------------+-----------------------+------------+ | view_id | post_id | viewer_id |...

Data Manipulation (SQL/Python)
0
0
8 people solved
Aug 4, 2025
Meta logo
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
Meta logo
Meta
Medium
Data Scientist

Demonstrate Culture Fit and Motivation in Meta Interview

Demonstrate Culture Fit and Motivation in Meta Interview Behavioral Interview: Culture Fit and Motivation (Meta — Data Scientist, Onsite) Prompt Answe...

Behavioral & Leadership
4
0
28 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Optimize Oculus Data Streaming with Bandwidth Constraints

Scenario Algorithmic screening for Meta VR/AR teams covering Oculus data streaming and geometric optimization. Question Oculus: Given an array frame_s...

Coding & Algorithms
4
0
47 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Chatbot's Retailer Value and Launch Viability

Evaluate Chatbot's Retailer Value and Launch Viability Scenario You are evaluating whether to launch a B2C chatbot for retailers on a commerce messagi...

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

Determine Probability of Video Selection and Impact Evaluation

Determine Probability of Video Selection and Impact Evaluation Video Recommendation Push: Selection Probabilities, Complements, and Design Choices Sce...

Statistics & Math
3
0
32 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Convince PM to Implement Duplicate Observation Tool

Convince PM to Implement Duplicate Observation Tool Scenario Meta is considering building a Duplicate Observation Tool (DOT) to detect malicious copy‑...

Analytics & Experimentation
4
0
32 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Rank Ads by Conversion Rate for Top 10 Performers

ad id | advertiser_id | created_at 1 | 101 | 2023-07-01 2 | 102 | 2023-07-05 3 | 101 | 2023-07-10 ​ impression id | a...

Data Manipulation (SQL/Python)
1
0
6 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Daily Visibility Score for Each Shop

shop_events | user_id | event_time | event_type | product_id | shop_id | |---------|------------|------------|------------|---------| | 101 | 2023...

Data Manipulation (SQL/Python)
2
0
9 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Unhealthy Oculus Usage with SQL Analysis

oculus_sessions +---------+---------------------+---------------------+---------+------------+ | user_id | session_start | session_end |...

Data Manipulation (SQL/Python)
0
0
6 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Daily Harmful Content View Percentage

VIDEOS +----+-------------+------------+ | id | uploader_id | is_harmful | +----+-------------+------------+ | 1 | 101 | true | | 2 | ...

Data Manipulation (SQL/Python)
0
1
8 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Improve Team Dynamics: Addressing Unwelcoming Behavior Effectively

Improve Team Dynamics: Addressing Unwelcoming Behavior Effectively Behavioral & Leadership (Meta, Data Scientist) — Onsite Scenario You are interviewi...

Behavioral & Leadership
4
0
32 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze User-Comment Distribution to Understand Engagement

Analyze User-Comment Distribution to Understand Engagement Meta DSPA Analytics Exercise: Comment Engagement Distribution Context You have three canoni...

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

Describe Facebook User Comment Distribution Shape and Justification

Describe Facebook User Comment Distribution Shape and Justification Characterizing Comments per User on Facebook Context You are analyzing the number ...

Statistics & Math
26
0
44 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Improve YoY Revenue Analysis with Complementary Metrics

ads_revenue +------------+-----------+ | date | revenue | +------------+-----------+ | 2023-01-01 | 120000 | | 2023-02-01 | 125500 | | 2...

Data Manipulation (SQL/Python)
101
0
241 people solved
Aug 4, 2025
Meta logo
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
166 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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