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
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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
Meta logo
Meta
Medium
Data Scientist

Describe Handling Cross-Functional Projects and Changing Priorities

Describe Handling Cross-Functional Projects and Changing Priorities This behavioral prompt evaluates how you collaborate across functions, respond to ...

Behavioral & Leadership
52
0
84 people solved
Jul 12, 2025
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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
82 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Compute Daily Revenue by Creation Source

active_ads date | ad_id | advertiser_id | creation_source | revenue 2023-09-01 | 1001 | 17 | mobile_app | 150.00 2023-09-01 | 1...

Data Manipulation (SQL/Python)
67
0
231 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Design SQL Query for Shop Visibility and User Activity Metrics

SHOP_VISIBILITY +----------+---------+------------+------------+-------------+--------------+ | user_id | shop_id | event_date | is_visible | signup_...

Data Manipulation (SQL/Python)
78
0
181 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Implement Clustered Sampling to Mitigate Network Effects in Testing

Implement Clustered Sampling to Mitigate Network Effects in Testing You are planning an A/B test for a new recommendation algorithm in a networked pro...

Analytics & Experimentation
24
0
99 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
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Meta
Medium
Data Scientist

Calculate Video Call Usage Metrics by Country and Date

video_calls +---------+-----------+------------+---------+----------+ | caller | recipient | ds | call_id | duration | +---------+-----------...

Data Manipulation (SQL/Python)
83
0
150 people solved
Jul 12, 2025
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Meta
Hard
Data Scientist

Determine Demand for WhatsApp Group Video-Calls

Determine Demand for WhatsApp Group Video Calls WhatsApp is considering launching group video calls. Assume the feature does not currently exist, but ...

Analytics & Experimentation
70
0
201 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Identify Unique Callers and French Customer Call Percentage

video_calls +---------+-----------+--------------+---------------------+---------------+ | call_id | caller_id | recipient_id | start_ts | ...

Data Manipulation (SQL/Python)
86
0
277 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist Locked

Identify Potential Users for Instagram Shopping Tab Adoption

Evaluates how to identify likely adopters of an Instagram Shopping tab and measure whether the feature creates incremental commerce value. Strong answ...

Analytics & Experimentation
33
0
92 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Identify Users Interested in Group Video Calls

video_calls caller | recipient | ds | call_id | duration u1 | u2 | 2023-09-01| c100 | 320 u3 | u4 | 2023-09-01| c101 ...

Data Manipulation (SQL/Python)
113
0
363 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Metrics to Evaluate Notification Impact on Users

Determine Metrics to Evaluate Notification Impact on Users Facebook sends several types of push notifications and is considering a new notification th...

Analytics & Experimentation
14
0
43 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Employ Collaborative Filtering for Personalized Recommendation Lists

Collaborative Filtering and Ranking for Personalized Recommendation Lists You are releasing a new recommendation feature that must generate personaliz...

Machine Learning
40
0
129 people solved
Jul 12, 2025
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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
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Meta
Medium
Data Scientist

Calculate Total Interactions for Each Product

Interactions +----------+-----------+------------+--------------+ | buyer_id | seller_id | product_id | interactions | +----------+-----------+-------...

Data Manipulation (SQL/Python)
56
0
182 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Define Success Metrics for Euro-Chat Customer-Service Chatbot

Success Metrics for the Euro-Chat Customer-Service Chatbot An e-commerce company deploys a customer-service chatbot called euro-chat to handle B2C sup...

Analytics & Experimentation
18
0
40 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Uncover User Needs for Group Calling Effectively

Uncover User Needs and Measure Group Calling Impact You are the product analyst for a messaging platform planning to introduce group calling. You need...

Analytics & Experimentation
113
0
304 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Define Metrics and Account for Network and Novelty Effects

Metrics for Notification-Triggered In-App Surveys Meta's notification system triggers optional in-app surveys to measure user sentiment after notifica...

Analytics & Experimentation
65
0
87 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Probabilities for Mixed Reviewer Types

Probabilities for Mixed Reviewer Types Two types of reviewers exist in a marketplace: - Lazy reviewers are 20% of reviewers and always give good revie...

Statistics & Math
84
0
220 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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