Meta Interview Questions

Meta System Design 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
Software Engineer

Maximize number with one digit swap

Maximize number with one digit swap Given a non-negative integer n (as an int or string), perform at most one swap of two digits to produce the maximu...

Coding & Algorithms
2
0
30 people solved
Jul 15, 2025
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Meta
Medium
Data Engineer

Write SQL and Python for data prep

Given clickstream events (user_id, event_type, ts, properties) and a users table (user_id, signup_ts, plan), write SQL to compute DAU/WAU/MAU, D1/W1 r...

Data Manipulation (SQL/Python)
1
1
8 people solved
Jul 15, 2025
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Meta
Medium
Data Scientist

Calculate Response Rate and Compare User Survey Ratings

USERS user_id | signup_date 10 | 2024-03-20 11 | 2024-04-01 12 | 2024-04-05 ​ SURVEYS survey_id | user_id | sent_at 1 | 10 ...

Data Manipulation (SQL/Python)
197
2
691 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze VR App Usage and Engagement Metrics

vr_usage +---------+------------+---------+------------+----------+ | user_id | date | app_id | session_id | duration | +---------+------------...

Data Manipulation (SQL/Python)
49
0
5 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Compute Shop Visibility Rate Using SQL and Python

shop_events | event_id | shop_id | user_id | event_type | event_time | | 1 | 101 | 1001 | view | 2023-07-01 10:05:00 | | ...

Data Manipulation (SQL/Python)
73
0
170 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
200 people solved
Jul 12, 2025
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Meta
Hard
Data Scientist

Analyze Data to Boost Group Post Comment Rates

Analytics Plan to Increase Group Post Comment Coverage A social shopping platform wants to increase the percentage of group posts that receive at leas...

Analytics & Experimentation
69
0
176 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze Recent Calling Behavior in France Using SQL

CALLS +---------+---------+---------------------+-------------------+----------+ | call_id | user_id | call_start_time | participant_cnt | is_vi...

Data Manipulation (SQL/Python)
68
0
184 people solved
Jul 12, 2025
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Meta
Hard
Data Scientist

Analyze Free Shuttle Impact on Employee Participation Rates

Analyze Free Shuttle Impact on Employee Participation Rates You have panel data for more than 1,000 company sites over time. Some sites adopt free shu...

Analytics & Experimentation
104
0
247 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate French User Engagement and U.S. Call Duration

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

Data Manipulation (SQL/Python)
79
0
377 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Average Session Length and Compare App Performance

user_sessions +---------+------------+------------+---------------------+---------------------+ | user_id | session_id | app | session_start ...

Data Manipulation (SQL/Python)
125
0
324 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze New Shops' Activity Compared to Existing Ones

shops +---------+------------+---------------+ | shop_id | created_at | category | +---------+------------+---------------+ | 1 | 2024-01-0...

Data Manipulation (SQL/Python)
53
0
8 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Identify Top 10 Users by Average Call Duration

video_calls | call_id | user_id | start_time | end_time | |---------|---------|----------------------|----------------------| | ...

Data Manipulation (SQL/Python)
6
0
29 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze Group Call Adoption Using SQL Queries

CALL_LOGS | call_id | user_id | call_start | call_end | is_group_call | participant_cnt | | 101 | 12 | 2023-08-01 10:00...

Data Manipulation (SQL/Python)
153
1
246 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze Conversation Engagement and Reaction Usage Effectively

messages +-----------+--------+----------+--------------+---------------------+ | messageid | sender | receiver | has_reaction | timestamp |...

Data Manipulation (SQL/Python)
173
3
404 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Weekly CTR and Campaign-Specific CTR in SQL

AdEvents ad_id | campaign_id | event | view_id | event_date 1 | 10 | impression | 123 | 2023-11-07 1 | 10 | cli...

Data Manipulation (SQL/Python)
66
2
244 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze Recent User Engagement in Video Calls

calls +-----------+-----------+---------------------+---------+---------+ | caller_id | callee_id | call_start_timestamp| country | call_id | +-------...

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

Analyze Hashtag Follow Behavior with SQL Queries

following_behavior +------------+---------+-----------+---------------+ | date | user_id | hashtag_id| hashtag_source| +------------+---------+-...

Data Manipulation (SQL/Python)
5
0
26 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze Recent Post Performance Using SQL Queries

INFO_STREAM_VIEWS +---------+-----------+--------------+----------+------------+ | post_id | viewer_id | relationship | duration | ds | +-----...

Data Manipulation (SQL/Python)
41
1
6 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Product Buyer Count and Interaction Percentage

interactions +-----------+----------+------------+----+------------+ | seller_id | buyer_id | product_id | li | create_date| +-----------+----------+-...

Data Manipulation (SQL/Python)
13
0
34 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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