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

Find Lowest Common Ancestor with Parents

Question LeetCode 1650. Lowest Common Ancestor of a Binary Tree III Compute the time and space complexity of your solution https://leetcode.com/proble...

Coding & Algorithms
1
0
11 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Solve grid shortest path and robot cleaning

Question LeetCode 1091. Shortest Path in Binary Matrix; LeetCode 489. Robot Room Cleaner https://leetcode.com/problems/shortest-path-in-binary-matrix/...

Coding & Algorithms
3
0
39 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Implement paginated, sortable dynamic table component

Implement paginated, sortable dynamic table component React Table Component: Pagination, Sorting, Dynamic Columns Context You are building a reusable ...

System Design
2
0
45 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Validate carpool capacity

Question LeetCode 1094. Car Pooling – Given trips[i] = [numPassengers, start, end] and an integer capacity, return true if the vehicle can fulfill all...

Coding & Algorithms
5
0
67 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Demonstrate behavioral competencies

Demonstrate behavioral competencies Behavioral Interview Prompt: Prepare STAR Stories Context You are preparing for an onsite Behavioral & Leadership ...

Behavioral & Leadership
4
0
38 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
Data Scientist

Analyze Recent User Activity from Video Call Logs

video_calls caller | recipient | ds | call_id | duration 123 | 456 | 2019-01-01 | 4325 | 864.4 032 | 789 | 2019-01-01 | 9395 | 263.7 456 | 032 | 2019-...

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

Calculate Ad Performance with Click-Through and Conversion Rates

impressions +---------+---------+---------------------+-----------+ | user_id | ad_id | event_time | platform | +---------+---------+-----...

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

Compare Instagram and Facebook Stories Using Key Performance Metrics

Compare Instagram and Facebook Stories Using Key Performance Metrics Scenario You are a data scientist tasked with quantitatively comparing the succes...

Analytics & Experimentation
2
0
40 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

Estimate Fake Accounts Using Data Signals and Sampling

Estimate Fake Accounts Using Data Signals and Sampling Estimating Fake Accounts on a Social Network Background A large social platform wants to estima...

Analytics & Experimentation
5
0
40 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Calculate Distinct High-View Posts and Spam View-Prevalence

content_views | user_id | post_id | view_count | view_date | | 101 | 572 | 3 | 2021-11-01 | | 102 | 732 | 5 | 2021-1...

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

Calculate and Compare Survey Response Rates for User Tenure

Surveys +--------+------------+--------------+----------+ | userid | date | survey_event | response | +--------+------------+--------------+----...

Data Manipulation (SQL/Python)
1
0
10 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

Calculate Response Rate and Compare New vs. Existing User Scores

survey_events +---------+------------+-----------+--------------+---------------------+ | user_id | is_new_user| responded | survey_score | event_time...

Data Manipulation (SQL/Python)
1
0
4 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

Analyze Ad CTR and Convert Transactions to USD

AdsImpressions +-----------+---------+------------+--------+-----------+ | user_id | ad_id | impressions| clicks | event_dt | +-----------+------...

Data Manipulation (SQL/Python)
126
0
500 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Evaluate Success of 'Similar Listings' Notification Feature

Evaluate Success of 'Similar Listings' Notification Feature Marketplace Analytics Case: "Similar Listings You May Like" Notifications Context You work...

Analytics & Experimentation
5
0
32 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

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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