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 EngineerIntern

Solve tree diameter and grid path problems

You are interviewing for a software engineer / ML intern role and are given the following algorithmic problems. --- Question 1: Longest Path in a Tree...

Coding & Algorithms
2
0
38 people solved
Nov 18, 2025
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Meta
Easy
Data Scientist Locked

Define hand-waving accuracy and launch decision

This question evaluates a data scientist's ability to define and operationalize detection metrics, design instrumentation and diagnostics, connect mod...

Analytics & Experimentation
4
0
52 people solved
Nov 16, 2025
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Meta
Hard
Software Engineer Locked

Guide a mouse to find cheese with APIs

This question evaluates the ability to reason about graph exploration and stateful API interaction in an unknown 2D grid, assessing competencies in se...

Coding & Algorithms
3
0
45 people solved
Nov 12, 2025
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Meta
Medium
Software Engineer

Determine if subarray sums to target

You are given an array of non-negative integers and a non-negative integer target. Determine whether there exists a contiguous subarray (continuous se...

Coding & Algorithms
7
0
59 people solved
Nov 11, 2025
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Meta
Medium
Software Engineer

Find K-th Largest and Longest Vacation

Solve the following two coding problems. 1. Find the k-th largest element Given an integer array nums and an integer k, return the element that wou...

Coding & Algorithms
3
0
29 people solved
Nov 1, 2025
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Meta
Medium
Machine Learning Engineer

Optimize repeated-value vectors and compute exclusive times

You are given two separate coding tasks from an interview. Task 1: Optimize storage for vectors with repeated values, then compute dot product You are...

Coding & Algorithms
3
0
49 people solved
Oct 30, 2025
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Meta
Hard
Data Scientist Locked

How to design Shop ad ranking

This question evaluates a candidate's expertise in machine learning and data science for ad ranking systems, including objective formulation and trade...

Machine Learning
3
0
28 people solved
Oct 26, 2025
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Meta
Medium
Software Engineer

Find shortest substring with N unique characters

You are given a string s and an integer n. Find the shortest contiguous substring of s that contains exactly n distinct characters. If there is no suc...

Coding & Algorithms
3
0
39 people solved
Oct 21, 2025
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Meta
Medium
Software Engineer Locked

Design a flight search platform

This question evaluates understanding of distributed systems, API and data model design, large-scale data ingestion and indexing, search ranking and c...

System Design
1
0
24 people solved
Oct 17, 2025
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Meta
Medium
Data Scientist

Write SQL to analyze shop visibility

You are given two tables. Use standard SQL (window functions allowed). Assume "today" is 2025-09-01 and that “currently visible” means a shop’s last s...

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

Write SQL for video-call recipients and FR activity

Given the schema and samples below, write ANSI‑SQL to answer both questions. Assume dates are stored in UTC. Today is 2025-09-01, so “yesterday” is 20...

Data Manipulation (SQL/Python)
61
1
569 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist Locked

Estimate Portal’s causal lift on video-call usage

This question evaluates applied causal inference and statistical analysis skills, including defining estimands, designing staggered-adoption differenc...

Statistics & Math
6
0
46 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL/pandas for KPI anomaly

Write SQL (and outline equivalent pandas) for a KPI anomaly investigation. Assume today = '2025-09-01'. Schema: Users(user_id INT, country TEXT, signu...

Data Manipulation (SQL/Python)
9
0
78 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist Locked

Diagnose a sudden KPI drop

This question evaluates operational analytics and experimentation competencies, including instrumentation and data-quality checks, de-seasonalization ...

Analytics & Experimentation
3
0
37 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for daily chats and fast replies

You are given a messaging events table that records one row per message sent. Schema - messages( date DATE, -- calendar date of event (UTC) ts TIMES...

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

Define and query shop visibility

You are given the following schema. Use only the columns provided; do not introduce new fields or labels. Tables and columns: - shops(shop_id INT, sho...

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

Write SQL for hashtag source and safety rates

Write SQL for the two tasks below. Assume the schema and sample data as given, and that “today” is 2025‑09‑01. Deduplicate exact duplicates by (date, ...

Data Manipulation (SQL/Python)
0
0
9 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist Locked

Design hashtag recommender with cold start

This question evaluates expertise in recommender-system design, feature engineering, ranking and learning-to-rank models, cold-start strategies, evalu...

Machine Learning
3
0
40 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL filtering, grouping, CASE, UNION tasks

Use the following schema and sample data to answer all parts. Assume standard ANSI SQL and that amounts are DECIMAL. Table: orders +----------+-------...

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

Control error under multiple testing

This question evaluates a candidate's understanding of multiple hypothesis testing, sequential monitoring, and error-rate control—specifically familyw...

Statistics & Math
2
0
33 people solved
Oct 13, 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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