Meta System Design 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."
Design measurement to detect fake accounts
Context You work on a social platform. The only product surface you can rely on is friend requests (sending/receiving/accepting/declining). Assume you...
Design a Coding Contest Platform
Design an online coding contest platform that supports programming competitions, code submission, automated judging, and live leaderboards. The platfo...
Explain your main growth area
What is one of your main growth or development areas right now? Explain: - What specific skill or behavior you are working to improve. - How you ident...
Design an A/B test for a new shop-ads algorithm
A new ranking/promotion algorithm will change which shop ads are shown (and their order). You are asked: “How do we know if this new algo is good?” De...
Compare Bayesian and frequentist decisions
A/B Test With Beta–Binomial Posteriors and Decision-Making Under Asymmetric Costs You ran a two-arm A/B test on a binary KPI with independent Beta(1, ...
Choose alternatives when randomization fails
Causal Impact of an Autoloaded Feature Without Clean Randomization Context You need to estimate the causal effect of a new autoloaded feature that is ...
Apply sequential testing without p-hacking
Sequential Monitoring With Early Stopping Context: You are planning a two‑sided hypothesis test with continuous monitoring and early stopping for effi...
Design robust group size limiting for calls
Design the admission-control and enforcement algorithm to limit group-call size under real-world race conditions. Constraints: multiple SFU edges in m...
Choose and compute recommender evaluation metrics
Restaurant Recommender: Offline Evaluation and Modeling Context: You are scoring p(y=1|x) with logistic regression to predict if a user will engage wi...
Decide when CTR falls but revenue rises
Ads-Ranking A/B Test: Decision, Decomposition, Diagnostics, and Exec Readout Context You ran a user-level A/B test of a new ads-ranking model. The tre...
Design a restaurant recommender under constraints
This question evaluates a candidate's competency in designing scalable machine learning recommender systems, covering retrieval and ranking architectu...
Estimate CTR lift with binomial tests and errors
A/B Test Inference, Peeking, and Multiple Comparisons You run a two-arm A/B test of click-through rate (CTR). - Control: n_c = 10,000,000 impressions,...
Lead a product deep dive with quantified impact
Behavioral Product Leadership Prompt (Data Scientist) You are interviewing for a Data Scientist role with a strong focus on product analytics, experim...
Describe failures, self-reflection, and conflict resolution
Answer the following behavioral prompts with recent examples: 1. Self-reflection / improvement - “In a recent project, where could you have done be...
Design a ranking and recommendation system
This question evaluates a candidate's competency in designing end-to-end ranking and recommendation systems, covering candidate generation, ranking, f...
Design an online auction platform
Design an Online Auction System Design a scalable, highly available online auction platform where users can list items for auction and other users can...
Describe a challenging project and work-style conflicts
Question This is the Meta Data Scientist onsite behavioral & leadership round. The anchor prompt is to describe your most challenging recent project e...
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 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...
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...