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
Hard
Machine Learning EngineerSenior+ AI Locked

Build a Friend Recommender

This question evaluates proficiency in graph algorithms, recommendation system logic, input validation, metric design, and test-driven software implem...

Coding & Algorithms
3
0
46 people solved
Mar 1, 2026
Meta logo
Meta
Medium
Data Scientist Locked

Analyze spend and creation-source shifts

This question evaluates a data scientist's competency in SQL-based data manipulation, time-series aggregation, joins, and metric computation for analy...

Data Manipulation (SQL/Python)
7
0
57 people solved
Mar 1, 2026
Meta logo
Meta
Medium
Data Scientist Locked

Evaluate AI-assisted ad creation

This question evaluates a candidate's competence in product analytics, causal inference, experimentation design, metric definition, and monitoring for...

Analytics & Experimentation
5
0
75 people solved
Mar 1, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Return right and left side views of a tree

This question evaluates understanding of binary tree data structures and the competency to identify side views by reasoning about which node is visibl...

Coding & Algorithms
4
0
39 people solved
Jan 5, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design a food ordering and delivery system

This question evaluates a candidate's ability to design scalable, reliable backend systems including API design, data modeling, state management, disp...

System Design
8
0
73 people solved
Jan 5, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Add two binary strings

This question evaluates proficiency with binary arithmetic and string manipulation, focusing on carry propagation and handling of very long inputs wit...

Coding & Algorithms
3
0
32 people solved
Jan 5, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Troubleshoot a website outage with disk full

This question evaluates operational incident response and systems troubleshooting skills, including understanding of disk/storage behavior, log and me...

Software Engineering Fundamentals
3
0
39 people solved
Jan 5, 2026
Meta logo
Meta
Medium
Data Scientist

Determine Superiority of Model A Using Hypothesis Testing

Hypothesis Test: Is Model A Better Than Model B? A search feature marks a session as successful only when both relevancy and accuracy binary flags equ...

Statistics & Math
26
0
103 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Impact of Targeting Ads to High-Intent Users

Evaluate Impact of Targeting Ads to High-Intent Users A product manager proposes allocating all ad impressions to users predicted to be high intent, a...

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

Evaluate Instagram's Short-Video Recommender System Success

Evaluate Instagram's Short-Video Recommender System Success Instagram is launching a short-video recommender feed. You are asked to choose metrics, re...

Analytics & Experimentation
110
0
412 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Machine Learning EngineerSenior+

Discuss conflicts, proudest project, and departure reasons

Behavioral & Leadership Questions — Machine Learning Engineer (Technical Screen) Answer the following prompts concisely, using concrete examples from ...

Behavioral & Leadership
4
0
55 people solved
Aug 11, 2025
Meta logo
Meta
Medium
Product Manager

Describe Tough Project and Tight Deadline

Behavioral Prompt: Challenging Project and Tight Timeline Tell me about your most challenging project. Also describe a time when you had to deliver un...

Behavioral & Leadership
6
0
44 people solved
Jul 1, 2025
Meta logo
Meta
Medium
Software Engineer

Describe teamwork and stress handling

Describe teamwork and stress handling This Meta Software Engineer behavioral question asks you to demonstrate, with concrete examples, how you collabo...

Behavioral & Leadership
7
0
71 people solved
Aug 7, 2025
Meta logo
Meta
Medium
Data Scientist

How would you measure Group Call success?

You are interviewing for a Data Scientist role at a social communication product similar to Meta. The team asks you to evaluate a Group Call feature t...

Analytics & Experimentation
2
0
36 people solved
Dec 26, 2025
Meta logo
Meta
Medium
Software Engineer

Solve sliding window and tree BFS

Solve sliding window and tree BFS Solve a typical medium-level sliding-window problem (e.g., longest substring without repeating characters). LeetCode...

Coding & Algorithms
5
0
50 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Identify Probability of Request Originating from Bad User

Identify Probability of Request Originating from Bad User Measuring Abuse in Friend-Requests: Bayes, Identification, and Precision Scenario A social-n...

Statistics & Math
5
0
35 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate New Model's Performance Against Existing System

Evaluate New Model's Performance Against Existing System Scenario You are evaluating a new machine-learning model that detects harmful content on a la...

Machine Learning
4
0
35 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Define Success Metrics for Circle Feature Evaluation

Define Success Metrics for Circle Feature Evaluation Scenario Measuring success and allocating resources for a new "Circle" posting feature in a socia...

Analytics & Experimentation
84
0
206 people solved
Aug 4, 2025
Meta logo
Meta
Easy
Data Scientist

Calculate Probability of Honest and Relevant Chatbot Answers

Calculate Probability of Honest and Relevant Chatbot Answers Chatbot Evaluation: Honesty and Relevance Scenario You are evaluating a customer-service ...

Statistics & Math
24
0
55 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate and Experiment with Harmful Content Detection Model

Evaluate and Experiment with Harmful Content Detection Model Evaluating a Harmful-Content Detection Model: Offline and Online Context You are given a ...

Machine Learning
75
0
149 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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