Meta Interview Questions

Meta Behavioral & Leadership 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 Company08.08.2026
Showing 20 results
Role
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Meta
Medium
Data Engineer

Recommend friends-of-friends

Question Given a dictionary such as {A:[B,C], B:[C,D], C:[E]}, return for a user U all people followed by U’s followees but not already followed by U....

Coding & Algorithms
3
1
65 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Count active follow connections

Question Write SQL to return the current number of active follow connections. Events table columns: user_id, target_id, event_type ('request_follow', ...

Data Manipulation (SQL/Python)
0
1
7 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Count uniques in sparse sorted array

Question A sorted array contains many duplicates but only a very small number of distinct values. Design an algorithm that counts how many unique numb...

Coding & Algorithms
29
0
10 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Maximize vacation streak with PTO

Question Given an array of characters containing 'w' (workday) and 'h' (holiday) and an integer n representing the number of PTO days you can convert ...

Coding & Algorithms
12
0
10 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Solve Data-Structure Problems in Python Interview Round

Scenario Interview coding round focusing on simple data-structure problems in Python. Question Given an integer, rearrange its digits (considering onl...

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

Analyze Bookstore Data to Identify Top Payment Methods

books +---------+------------+----------+-------+ | book_id | title | author_id| price | +---------+------------+----------+-------+ | 1 | ...

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

Forecast Next Year's Revenue Using YoY% Analysis

ad_revenue +------------+---------+ | date | revenue | +------------+---------+ | 2023-01-01 | 1000 | | 2023-01-02 | 1200 | | 2024-01-01 |...

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

Compute Effective Reads with SQL and Python Streaming

post_read_events +----------+---------+-----------+---------+--------------------+---------------------+---------------------+ | event_id | user_id | ...

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

Analyze Cross-Country Call Data for Recent Trends

Users +---------+-------------+----------+ | user_id | signup_date | country | +---------+-------------+----------+ | 1 | 2021-01-04 | US ...

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

Overcome Challenges and Build Trust in Teamwork

Overcome Challenges and Build Trust in Teamwork Behavioral Interview: Teamwork, Feedback, Trust, and Conflict (Data Scientist) Context You are intervi...

Behavioral & Leadership
2
0
44 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Calculate Shop Visibility Ranking in Search Results

shop_impressions +------------+---------+----------+--------+ | date | shop_id | position | clicks | +------------+---------+----------+--------...

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

Analyze User Engagement and Spammer Read-Rate in SQL

messages +------------+------------+-------------+--------------+-----------+ | sender_id | receiver_id| message_id | sent_date | read_date | +--...

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

Identify Duplicate Posts by User and Date

posts +---------+---------+---------------------+---------------+ | post_id | user_id | created_at | content | +---------+---------+---...

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

Calculate Average Session Duration and Performance Metrics

user_sessions +---------+------------+------+---------------------+---------------------+ | user_id | session_id | app | start_time | end_ti...

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

Design A/B Test for Short-Video Recommendation Algorithm

Design A/B Test for Short-Video Recommendation Algorithm A/B Test: New Short‑Video Recommendation Algorithm Context You are evaluating a new recommend...

Analytics & Experimentation
8
0
72 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Evaluate Instagram Shopping Tab Success with Key Metrics

Evaluate Instagram Shopping Tab Success with Key Metrics Instagram Shopping Tab: Post-Launch Evaluation and Sizing Context You are evaluating the succ...

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

Identify Shops with Low Weekly Visibility Rates

SHOP_VISIBILITY +-----------+---------+------------+---------+ | shop_id | user_id | view_date | visible | +-----------+---------+------------+----...

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

Generate Daily Unique User Views for Each Shop

shop_views +---------+---------+---------------------+ | user_id | shop_id | view_time | +---------+---------+---------------------+ | 101 ...

Data Manipulation (SQL/Python)
69
0
253 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Total Revenue in USD Using SQL Query

ads_revenue +---------+------------+---------+----------+ | ad_id | country | revenue | currency | +---------+------------+---------+----------+ ...

Data Manipulation (SQL/Python)
100
1
192 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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