Meta Behavioral & Leadership Interview Questions
Practice 1,166 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."
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"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 Handling Unexpected Feedback and Actions Taken
Describe Handling Unexpected Feedback and Actions Taken Behavioral: Resilience After Unexpected Negative Feedback or Rejection Context You are in an o...
Define metrics for harmful-content severity
Context You are a Data Scientist on the integrity / harmful-content team for a large social media product. Leadership wants a way to track how bad pol...
Diagnose spend drops, bots, and Stories
This question evaluates a product data scientist's competencies in diagnostic product analytics, advertising measurement and attribution, bot and abus...
Implement BST Iterator and Ticket Queue
This question evaluates understanding of binary search tree traversal and iterator design with amortized time and space analysis, as well as dynamic p...
Solve Subarray Sum and Local Minimum
Two coding problems were reported in the same phone-screen round: 1. Count target-sum subarrays. Given an integer array nums and an integer k, return ...
Design a weapon-ad harmful content detection system
This question evaluates skills in end-to-end system design and applied machine learning for multi-modal harmful content detection, covering scalabilit...
Deep copy a linked list with random pointers
This question evaluates understanding of linked-list structures, pointer/reference manipulation, deep versus shallow copying, and the ability to analy...
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...
Design place-of-interest ML system
Design place-of-interest ML system Design a POI (Places of Interest) Recommendation System Context Design a global POI recommender for a mobile maps/f...
Explain Central Limit Theorem's Importance in A/B Testing
Explain the Central Limit Theorem's Importance in A/B Testing This statistics prompt asks you to state the Central Limit Theorem, explain why it matte...
Fake Accounts [AE]
Evaluates probability, classification metrics, and feature engineering for fake-account detection. Strong answers apply Bayes' rule with rate-weighted...
Analyze User Comment Distribution and Sampling Effects
Analyze User Comment Distribution and Sampling Effects You are analyzing daily comment counts per user. The individual user-level distribution is righ...
Track Success and Guardrail Metrics for Push Notifications
Track Success and Guardrail Metrics for Push Notifications You are designing and evaluating a new push-notification feature for a travel-recommendatio...
Determine Probability of Shared Videos in Recommendations
Meta statistics and product prompt on video recommendation overlap, covering combinations, probability of shared videos, expected intersection size, s...
Compute unmatched parentheses length
This question evaluates string-processing skills and understanding of parentheses matching and cancellation, focusing on linear-time sequence analysis...
Design coding platform with global leaderboard
This question evaluates proficiency in scalable distributed systems and system design, including real-time ranking algorithms, data modeling, API desi...
Traverse binary tree by levels with constraints
Given a binary tree and a predicate P(node), perform a level-by-level traversal that collects, for each level, only the nodes whose values satisfy P. ...
Describe background and job motivations
Behavioral HR Screen: Self-Introduction, Role, Motivation, and Company Fit Context - Scenario: HR screen for a Software Engineer role. - Goal: Concise...
Design a basic bank system API
In-Memory Bank System: API, Data Model, Invariants, Semantics, and Edge Cases Goal Design and implement an in-memory bank system that supports account...
Design a scalable banking system
System Design: Core Banking Platform Problem Design a banking system that supports: - Account creation - Balance inquiry - Deposit and withdrawal - At...