Meta Interview Questions

Meta Analytics & Experimentation 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
Data Scientist

Compute callers contacting >3 people last 7 days

Using the schema below, write a single SQL query to return the number of unique callers who started calls with more than 3 distinct other users during...

Data Manipulation (SQL/Python)
0
0
4 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Implement randomized Quickselect without k-shift bug

Implement randomized Quickselect to return the k-th largest element (1-based k, 1 ≤ k ≤ n) from an unsorted integer array. Use an in-place partition t...

Coding & Algorithms
4
0
47 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute 95th-percentile call concurrency

Given N call sessions as half-open intervals [start, end) in UNIX seconds, design an algorithm to compute the 95th percentile of per-minute concurrent...

Coding & Algorithms
3
0
39 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for 7-day WhatsApp call metrics

Today is fixed as 2025-09-01. Using PostgreSQL, write a single query that returns one row per UTC calendar date for the last 7 days inclusive of today...

Data Manipulation (SQL/Python)
0
0
4 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for revenue and advertiser analyses

Use the schema below and ANSI SQL. Treat “today” as 2025-09-01. Schema: - active_ads(date DATE, ad_id INT, advertiser_id INT, creation_source VARCHAR,...

Data Manipulation (SQL/Python)
0
0
6 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for initiators and French DAU%

You are given the following PostgreSQL tables. Assume all timestamps are UTC and "today" is 2025-09-01. For any reference to "last 7 days," use the in...

Data Manipulation (SQL/Python)
0
1
6 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Define and analyze new-vs-existing activity

Ambiguous product question: Are existing users more active than new users over the last 28 days (ending today = 2025-09-01)? 1) Propose two reasonable...

Data Manipulation (SQL/Python)
3
0
54 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for comment analytics

You are given the following schema and tiny sample data. Schema: - users(user_id INT PRIMARY KEY, country VARCHAR, created_at DATE) - posts(post_id IN...

Data Manipulation (SQL/Python)
0
0
6 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist Locked

Build a Bayes classifier for reviewer types

This question evaluates Bayesian inference skills, including posterior updating under conditional independence, likelihood modeling for categorical ob...

Machine Learning
2
0
24 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write advanced SQL for sales support analytics

Write SQL for the following schema and tasks. Assume ANSI SQL with DATE_TRUNC and INTERVAL supported. Sample tables (minimal rows shown). accounts +--...

Data Manipulation (SQL/Python)
0
0
6 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute shop visibility and intent metrics in SQL

Schema (PostgreSQL). Tables: users(user_id) shops(shop_id, shop_name, merchant_type) posts(post_id, shop_id, is_shoppable BOOLEAN, created_at TIMESTAM...

Data Manipulation (SQL/Python)
0
0
7 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL to infer group-call demand

You are given only 1:1 call logs and a user table. Use SQL to estimate latent demand for a 'Group Call' feature by detecting 10-minute 'call loops' wh...

Data Manipulation (SQL/Python)
1
0
4 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for social feed metrics and ties

You are given the following schema (PostgreSQL) and sample rows. Assume UTC timestamps and that friendships are static over the sample window. users(u...

Data Manipulation (SQL/Python)
1
0
13 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute multi-account activity and unread percentages in SQL

You are given two tables. Use them as the source of truth and do not assume any other data. Table: notifications +--------+------------+------------+-...

Data Manipulation (SQL/Python)
2
0
24 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute multi-account actives and unread coverage

You have two tables. Table: notifications +--------+------------+------------+-------------------+--------+ | userid | ds | time | notification_type |...

Data Manipulation (SQL/Python)
4
0
37 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL to flag coordinated fake accounts

Assume today is 2025-09-01. Schema and tiny samples: users(user_id, created_at, country) 1 | 2025-07-01 | US 2 | 2025-08-10 | IN 3 | 2025-08-15 | US 4...

Data Manipulation (SQL/Python)
0
0
7 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Compute 7-day views and reactions by relationship

Use the schemas and sample data below to answer two tasks. Assume dates are strings in 'YYYY-MM-DD'. Treat "today" as 2025-09-01; "last/past 7 days" m...

Data Manipulation (SQL/Python)
0
0
5 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Join datasets and compute conversion by assignment

You are given two CSVs. Create tables and write SQL to produce both visit-level and visitor-level conversion datasets, then aggregate conversion by as...

Data Manipulation (SQL/Python)
0
0
5 people solved
Oct 13, 2025
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Meta
Medium
Software Engineer

Merge two interval lists into a union

You are given two lists of closed intervals [start, end]. Each list is individually sorted by start and contains non-overlapping intervals. Merge the ...

Coding & Algorithms
1
0
23 people solved
Sep 6, 2025
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Meta
Medium
Machine Learning Engineer

Optimize ribbon piece length by binary search

Given an array lengths of positive integers and an integer k, you may cut each element into pieces of integer length L > 0. Find the maximum L such th...

Coding & Algorithms
3
0
48 people solved
Sep 6, 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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