Meta Data Scientist Machine Learning Interview Questions
Practice 50 real Machine Learning interview questions for Data Scientist roles at Meta.
"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 a System to Recommend Local Restaurant Profiles
Design a System to Recommend Local Restaurant Profiles Recommending Local Restaurant Pages in the News Feed Context Design a non-ads recommendation sy...
Design an ad recommendation and ranking system
You are building an ad recommendation/ranking system for a content feed (e.g., short-form videos). At each feed position, you may show either an organ...
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 ...
Which clustering algorithm would you use and why
Question You need to cluster users for a social product (e.g. Meta) to discover meaningful groups such as communities, interest groups, or usage segme...
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...
How would you design a Shop Ads ranking algorithm?
This question evaluates a candidate's understanding of machine learning-driven ad ranking, auction mechanics, multi-stakeholder objective formulation,...
Tune fraud threshold under review capacity and costs
Fraud Triage Thresholding with Calibrated Scores Context You have a fraud model that outputs a calibrated score s ∈ [0, 1] per account, where s ≈ P(fa...
Evaluate Fake-Account Classifier with Precision and Recall Metrics
Evaluate Fake-Account Classifier with Precision and Recall Metrics Evaluating a Fake-Account Classifier in Production Scenario You have trained a mode...
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...
Replace legacy ads model safely
Facebook Ads Ranking Replacement: M0 to M1 You are asked to replace a legacy ads ranking model (M0) with a new model (M1) in a large-scale feed ads sy...
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...
Determine Features for Effective Hashtag Recommendations
Hashtag Recommendation System Design You are designing a hashtag recommendation system for a social-media platform. Given a user composing post conten...
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 ...
Design an ad recommendation ranking approach
This question evaluates competency in machine-learning driven ad ranking and recommendation systems, including objective formulation, modeling strateg...
Identify Algorithms for Detecting Malicious Duplicated Content
Identify Algorithms for Detecting Malicious Duplicated Content Detecting Malicious Duplicated Text (DOT) Scenario You are selecting technical approach...
Identify Fake Accounts Using Machine Learning Techniques
Identify Fake Accounts Using Machine Learning Techniques Scenario You are a data scientist at Meta. Fake accounts (bots, spam, scams, impersonation, c...
How to design Shop ad ranking
This question evaluates a candidate's expertise in machine learning and data science for ad ranking systems, including objective formulation and trade...
Choose Metrics for Evaluating Fake-User Classifier
Choose Metrics for Evaluating a Fake-User Classifier A sudden spike in daily average comments may be driven by fake users. You are asked to build a bi...
Build Predictive Model for Buyer Engagement Uplift
Predict Engagement Uplift for a New "Show Similar Products" Button A new "Show similar products" button may change buyer engagement. You need to build...
Propose an ads recommendation model for shop ads
You need to propose a modeling approach for recommending/ranking shop ads (i.e., which shop ads to show and in what order) for a marketplace app. Desc...