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
Meta logo
Meta
Medium
Data Scientist

Calculate Engagement Metrics for Info-Stream Content Analysis

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

Data Manipulation (SQL/Python)
107
1
243 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Compute Daily Revenue by Creation Source

active_ads date | ad_id | advertiser_id | creation_source | revenue 2023-09-01 | 1001 | 17 | mobile_app | 150.00 2023-09-01 | 1...

Data Manipulation (SQL/Python)
67
0
230 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate the Health of Facebook Groups

Group Health Metrics and Threaded Comments Experiment You are a Data Scientist working on a platform with Groups ranging from small hobby clubs to ver...

Analytics & Experimentation
52
0
51 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Quality and frequency control for push notifications

Push Notifications: Quality, Overload Mitigation, and Per-user Caps You are a data scientist working on push notifications for a consumer app. Pushes ...

Analytics & Experimentation
56
0
124 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Design a Restaurant Recommendation System for Food Apps

Design a Restaurant Recommendation System for a Food-Ordering App You are designing an end-to-end recommendation system that suggests restaurants to u...

Machine Learning
34
0
101 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Resolve Team Conflicts to Improve Delivery Efficiency

Behavioral Interview: Resolve Team Conflict and Improve Delivery You are in a Behavioral and Leadership interview for a Data Scientist role. The inter...

Behavioral & Leadership
32
0
104 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist Locked

Evaluate Chatbot Launch: Value, Risks, Impact, Success Metrics

Meta analytics prompt on evaluating a retailer-facing chatbot launch, covering opportunity sizing without A/B testing, user and business metrics, mode...

Analytics & Experimentation
54
0
73 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Identify Users Interested in Group Video Calls

video_calls caller | recipient | ds | call_id | duration u1 | u2 | 2023-09-01| c100 | 320 u3 | u4 | 2023-09-01| c101 ...

Data Manipulation (SQL/Python)
113
0
363 people solved
Jul 12, 2025
Meta logo
Meta
Easy
Data Scientist

Posterior probability given model accuracy

Security Classification: Posterior Probability When Flagged You are evaluating a binary classifier that flags potentially bad users. Assume: - Base ra...

Analytics & Experimentation
7
0
30 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Define Metrics and Account for Network and Novelty Effects

Metrics for Notification-Triggered In-App Surveys Meta's notification system triggers optional in-app surveys to measure user sentiment after notifica...

Analytics & Experimentation
65
0
86 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Design Experiment to Test New Hashtag Recommender Algorithm

Experiment Design: Testing a New Hashtag Recommender A social app shows hashtag recommendations to users while they compose posts. A new algorithm is ...

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

Calculate Probabilities for Mixed Reviewer Types

Probabilities for Mixed Reviewer Types Two types of reviewers exist in a marketplace: - Lazy reviewers are 20% of reviewers and always give good revie...

Statistics & Math
84
0
219 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Leverage Data Sources for Effective Push Notification Strategy

Data Sources and Metrics for Push Notification Strategy A product team wants to improve the quality and impact of mobile push notifications for a cons...

Analytics & Experimentation
8
0
36 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Distribution of Daily Page Shares Per User

Engagement Distributions and Cohort Dynamics You are analyzing per-user, per-day engagement. Assume the panel includes all users, inactive days count ...

Statistics & Math
87
2
127 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Design and Validate Initial Restaurant Recommendation Model

Design and Validate an Initial Restaurant Recommendation Model You are designing a first-iteration machine-learning model to recommend restaurants to ...

Machine Learning
28
0
110 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Video Call Usage Metrics by Country and Date

video_calls +---------+-----------+------------+---------+----------+ | caller | recipient | ds | call_id | duration | +---------+-----------...

Data Manipulation (SQL/Python)
83
0
150 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Advertising for local businesses boosting popular posts

Boosting Popular Posts for Local SMBs You are evaluating an experiment where small local businesses can pay to boost their popular organic posts. Defi...

Analytics & Experimentation
102
0
315 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate the Success of Instagram Checkout

Evaluating Instagram Checkout Instagram Checkout allows users to discover products, add to cart, pay, and manage post-purchase flow without leaving In...

Analytics & Experimentation
89
0
94 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Product Manager

Product Metrics & Debugging Scenarios

Product Metrics and Debugging Scenarios You are a PM candidate evaluating data, metrics, and operational plans for large-scale consumer products. Answ...

Product / Decision Making
11
0
52 people solved
Jul 4, 2025
Meta logo
Meta
Hard
Product Manager

Design Parking for Google Maps

Design a parking-finding experience for Google Maps. Assume the product goal is to help drivers reduce uncertainty and wasted time when parking near a...

Product Design & Strategy
4
0
32 people solved
Feb 22, 2024

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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