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
Machine Learning Engineer

Answer behavioral questions on projects and feedback

Prepare to answer common behavioral questions, with follow-up probing for details: - Describe a project you’re most proud of. - Describe a project whe...

Behavioral & Leadership
4
0
64 people solved
Jan 5, 2026
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Meta
Easy
Product Analyst

Design experiments and diagnose metric changes

You are a Product/Data Scientist at a food-delivery marketplace (customers, dashers/couriers, merchants). Answer the following product analytics & exp...

Analytics & Experimentation
12
0
147 people solved
Feb 22, 2026
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Meta
Hard
Data Scientist

Compute Heavy-Caller Percentages

You are given two tables that track voice calls and daily active users for a messaging app. Table: call_events - call_id BIGINT — unique call identifi...

Data Manipulation (SQL/Python)
7
0
56 people solved
Jan 2, 2026
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Meta
Easy
Data Scientist

Evaluate account re-ranking via logs and A/B test

A product has users with multiple accounts. In the UI, these accounts are shown as a list. - Current ranking: accounts are sorted by most recent visit...

Analytics & Experimentation
7
0
61 people solved
Feb 3, 2026
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Meta
Easy
Product Analyst

Tell me about a high-impact end-to-end project

Question Tell me about a high-impact project that you personally drove end-to-end. Walk through the full lifecycle and be ready to cover each of the f...

Behavioral & Leadership
5
0
44 people solved
Feb 2, 2026
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Meta
Medium
Data ScientistSenior+

Describe influencing without authority

Behavioral (STAR) Prompt: Disagreeing With a Senior Engineer's Design Without Authority Context You are interviewing for a Data Scientist role in an o...

Behavioral & Leadership
10
0
163 people solved
Oct 13, 2025
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Meta
Easy
Analytics Engineer Locked

Design and evaluate an ads ranking algorithm

This question evaluates a candidate's proficiency in designing and evaluating production-scale ads ranking systems within Machine Learning, covering r...

Machine Learning
12
0
88 people solved
Feb 15, 2026
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Meta
Medium
Software Engineer

Explain key ML metrics and techniques

You are asked a set of short conceptual machine learning questions. 1. Confusion matrix and metrics For a binary classification problem: - Def...

Machine Learning
7
0
67 people solved
Dec 8, 2025
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Meta
Easy
Data Scientist Locked

How would you design a Shop Ads ranking algorithm?

This question evaluates a candidate's understanding of machine learning-driven ad ranking, auction mechanics, multi-stakeholder objective formulation,...

Machine Learning
7
0
80 people solved
Feb 12, 2026
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Meta
Medium
Software Engineer

Merge Three Sorted Arrays Without Duplicates

Merge Three Sorted Arrays Without Duplicates You are given three integer arrays sorted in nondecreasing order. Merge them into one sorted array that c...

Coding & Algorithms
0
0
9 people solved
Mar 2, 2026
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Meta
Medium
Data Scientist

Design an ad recommendation and ranking system

You are building an ad recommendation/ranking system for a content feed (e.g., short-form videos). At each feed position, you may show either an organ...

Machine Learning
9
0
81 people solved
Oct 20, 2025
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Meta
Medium
Machine Learning Engineer

Solve Two String Problems

The interview included two coding questions: 1. Exactly one edit apart Given two strings s and t, determine whether they are exactly one edit apart...

Coding & Algorithms
4
0
47 people solved
Apr 12, 2026
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Meta
Medium
Machine Learning Engineer Locked

Design a weapon-sale ad detection system

This question evaluates a candidate's competence in end-to-end machine learning system design, covering multimodal signal integration (text, images, b...

ML System Design
14
0
133 people solved
Jan 5, 2026
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Meta
Medium
Machine Learning Engineer

Find target using robot movement API

You control a robot in an unknown 2D grid. The grid layout and boundaries are unknown, and you cannot access the map directly. Some cells are blocked ...

Coding & Algorithms
12
0
93 people solved
Oct 30, 2025
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Meta
Medium
Software Engineer

Design an online chess platform

Design an online chess platform. Requirements: - User matchmaking (ranked/unranked). - Real-time gameplay with low latency moves. - Enforce game rules...

System Design
6
0
53 people solved
Feb 25, 2026
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Meta
Medium
Software Engineer

Design a real-time game leaderboard

Design a real-time leaderboard for an online game. Requirements: - Players submit score updates frequently. - Query top N players globally and per reg...

System Design
10
0
83 people solved
Feb 25, 2026
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Meta
Hard
Product Analyst

Evaluate WhatsApp Group Video Calling

Meta is considering improvements to WhatsApp group video calling. The product team wants to understand whether users need this feature, how to increas...

Analytics & Experimentation
3
0
23 people solved
Mar 15, 2026
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Meta
Medium
Software Engineer Locked

Compute Exclusive Execution Times

This question evaluates understanding of nested function execution and precise exclusive-time accounting derived from chronological logs, emphasizing ...

Coding & Algorithms
0
0
15 people solved
May 25, 2026
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Meta
Medium
Software EngineerSenior+

Handle conflict and missed delivery deadlines

Answer behavioral questions covering: 1. Conflict: Describe a time you had significant conflict with a teammate/stakeholder. What caused it, what did ...

Behavioral & Leadership
6
0
58 people solved
Feb 11, 2026
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Meta
Medium
Data Scientist

Compute Each Advertiser's Share of Shop Ad Spend

Compute Each Advertiser's Share of Shop Ad Spend You have the following daily advertising table: `text ads_detail( advertiser_id, ad_id, ad_type...

Data Manipulation (SQL/Python)
1
0
14 people solved
May 22, 2026

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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