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
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Meta
Medium
Software Engineer

Solve two string algorithm tasks

Solve two string algorithm tasks Answer both parts. A) Parentheses correction: Given a string s consisting only of '(' and ')', output the minimum num...

Coding & Algorithms
3
0
25 people solved
Jul 15, 2025
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Meta
Medium
Software Engineer

Implement weighted random index picker

Implement weighted random index picker Design a class WeightedPicker that is initialized with an array of positive integer weights w and supports pick...

Coding & Algorithms
2
0
24 people solved
Jul 15, 2025
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Meta
Medium
Data Scientist

Design "Restaurants You May Know" Recommendation Algorithm

Design "Restaurants You May Know" Recommendation Algorithm A food-delivery app wants to launch a personalized home-page module called "Restaurants You...

Analytics & Experimentation
36
0
126 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
Medium
Data Scientist

Evaluate Facebook's Restaurant Recommendations Feature Effectiveness

Experiment Design: Restaurant Recommendations in Facebook News Feed Facebook is considering restaurant recommendation units inside News Feed, such as ...

Analytics & Experimentation
12
0
36 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

Expected round of first selection in repeated sampling

Random Perk Selection: Expected First Round There are 1,000 employees. Each round, 10 distinct employees are selected at random for a perk. No one can...

Analytics & Experimentation
13
0
36 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Survey Response Rate and Quality Metric in SQL

survey_responses +---------+----------+---------------------+---------------------+-------+ | user_id | survey_id| impression_ts | click_ts ...

Data Manipulation (SQL/Python)
70
0
159 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

Determine Old vs. New Users' Shop Visibility Changes

SHOP_VISIBILITY_HISTORY +----------+----------------+---------------------+-------------------+---------+ | user_id | user_signup_dt | action_timesta...

Data Manipulation (SQL/Python)
61
0
5 people solved
Jul 12, 2025
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Meta
Easy
Data Scientist

Expected impressions per user under random assignment

Random Assignment of Ad Impressions Across Users In an A/B experiment, Y ad impressions are served uniformly at random across X distinct users. Each i...

Analytics & Experimentation
14
0
31 people solved
Jul 12, 2025
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Meta
Hard
Data Scientist

Evaluate Factors Before Replacing Recommendation Model

Evaluate Factors Before Replacing a Recommendation Model A large ads platform has built a new recommendation or ranking model and plans to deprecate t...

Machine Learning
62
0
273 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

Expected meetings in Room 1 after random assignment

Expected Meetings in Room 1 Conditional on Being Non-empty There are N rooms and k meetings. Each meeting independently chooses a room uniformly at ra...

Analytics & Experimentation
12
0
32 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
Medium
Data Scientist

Calculate Weekly Thread Engagement with Reactions in SQL

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

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

Calculate Recent Post Views and Reactions for Social Media

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

Data Manipulation (SQL/Python)
87
0
320 people solved
Jul 12, 2025
Meta logo
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
Meta logo
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
180 people solved
Jul 12, 2025
Meta logo
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

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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