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.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Describe cross-team collaboration and learning from failure
Answer the following behavioral prompts using specific examples from your experience: 1. Cross-team collaboration: Tell me about a project where you w...
Design nearby place recommendations
Real‑Time Nearby Places Recommendation System Context Design a mobile feature that recommends nearby places (e.g., restaurants, shops, attractions) to...
Design a price tracking system
Question Design a price tracking system for e-commerce sites (similar to price-history tools such as CamelCamelCamel or Keepa). The system ingests pro...
How would you evaluate a new ads ranking algorithm?
Context You work at a social network company with an ads marketplace. The company has an existing ads ranking algorithm currently used to select and o...
Analyze Product Growth Cases
This question evaluates product analytics and experimentation competencies for a Product Analyst role, including metric definition, funnel decompositi...
Determine Success Metrics for Circle Feature Optimization
Determine Success Metrics for Circle Feature Optimization Scenario Meta is evaluating a new social feature called Circle (similar to Facebook Groups),...
Validate abbreviations and brackets
The coding round included two short implementation problems: 1. Abbreviation validation Given a lowercase word word and a string abbr, determine wheth...
Answer core Meta behavioral questions
You are in a behavioral interview for a software engineering role. Answer the following questions with concrete examples from your experience (interns...
How would you design Shop-ad ranking?
Suppose the previous experiment shows that, in some contexts, users are more likely to convert when shown an ad that leads to an in-app Shop rather th...
Apply reinforcement learning to product decisions
This question evaluates expertise in reinforcement learning and sequential decision-making for product optimization, covering MDP formulation, contras...
Detect and address Simpson’s paradox
Experiment Aggregation Bias and Heterogeneity: Simpson's Paradox, Robust Estimation, and Decisioning Context You ran a randomized experiment measuring...
Explain why LASSO selects features
Explain why LASSO performs feature selection. Provide: 1) high-level intuition comparing L1 vs. L2 penalties; 2) geometric interpretation of the const...
Describe a leadership STAR story
Behavioral & Leadership: Protecting Analytical Rigor Under Deadline (STAR) Context: You are interviewing for a Data Scientist role. The interviewer wa...
Increase posts receiving comments via experimentation
Increase the Share of Posts That Receive a Meaningful Comment You are a data scientist for a consumer social app with posts and comments. Your goal is...
Analyze DAU comments distribution and resampling
Consider the metric comments_per_DAU (number of comments a daily active user makes in a day). a) Shape: Describe and justify the expected distribution...
Build predictive model for feature rollout targeting
Before global launch, you want to predict which users or products would benefit most from the 'More like this' button so you can stage rollout. Design...
Design pre-launch plan and cluster A/B test
A Facebook feature ('More like this' button that surfaces similar products) is being considered for Instagram, but it has not launched on Instagram. Y...
Demonstrate leadership in cross-functional collaboration
Question This is the Meta Data Scientist onsite behavioral & leadership round. The interviewer works through a set of leadership prompts and expects y...
Select interest thresholds under skewness and cost
Profit-Optimal Threshold Selection from an Interest Score You have a per-user interest_score s ∈ [0, 1] for a new feature. The score distribution appe...
Choose tests and solve distribution parameters
Engagement Comparison: New vs Existing Users (2025-08-05 → 2025-09-01) Context: You have per-user daily session counts (integer, skewed, many zeros) f...