Data Scientist Machine Learning Interview Questions
Practice 399 real Machine Learning interview questions for Data Scientist roles. From companies including Meta, Amazon, Google, Capital One, TikTok.
"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."
Explain Deep Learning to a 5-Year-Old Child
Microsoft Phone-Screen: Machine Learning Fundamentals You are interviewing for a machine learning/data science role and should provide concise, struct...
Identify Fake Accounts Using Machine Learning Techniques
Scenario You are a data scientist at Meta. Fake accounts (bots, spam, scams, impersonation, coordinated inauthentic behavior, and compromised legitima...
Explain Overfitting and Underfitting in Machine Learning
ML Fundamentals and Computer Vision: Core Concepts Instructions You are interviewing for a data science role focused on classical ML and computer visi...
Design a Real-Time Personalized Ad Selection System
End-to-End ML System Design: Real-Time Ad Selection Context You need to design a real-time, data-driven ad selection system that personalizes ads for ...
Handle missing values for LGD modeling
Handling Missing Values for LGD Modeling Context You are building a Loss Given Default (LGD) model using account- and borrower-level features captured...
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...
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...
Derive and regularize logistic regression
Churn Propensity with Logistic Regression: Theory, Validation, and Decisions Context: You are building a churn propensity model (y ∈ {0,1}) using logi...
Build and evaluate illegal-video classifier
End-to-End ML System Design: Flag Illegal YouTube Videos You are tasked with designing a production ML system to detect and triage potentially illegal...
Explain random forests, bagging, and evaluation
Random Forests, Bagging vs Boosting, and Practical Model Validation You are building a supervised learning model on tabular data. Explain and compare ...
Decide standardization, sparse numerics, correlated features
You are given a tabular dataset for supervised learning with features: F1 (counts, mostly small integers with many zeros), F2 (monetary amounts in dol...
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 and select K in K-means
K-means Clustering: Concepts, Initialization, Model Selection, Preprocessing, and Business Validation Context: You are clustering customer data with n...
Explain an ML project end-to-end with tradeoffs
Pick one of your production ML projects and walk through it end-to-end. Be specific: 1) Problem framing (prediction vs causal decisioning), target def...
Design end-to-end regression for energy demand
End-to-End Daily Energy Prediction for Commercial Buildings Context You are asked to design and justify an end-to-end regression system that predicts ...
Model preference without ground truth
Problem: Designing an Uplift Modeling and Evaluation Strategy for Event Notifications Without Ground-Truth Labels You need to decide which users shoul...
Build and deploy an uplift targeting model
Uplift Modeling and Policy Design for Free Trial/Bonus Targeting You ran a past randomized test that offered some users a free trial/bonus (treatment)...
Build a Bayes classifier for reviewer types
Bayesian Reviewer-Type Inference Setup There are two reviewer types: - Lazy (prior probability 0.20): always gives a "Good" review. - Careful (prior p...
Evaluate fraud classifier with cost-sensitive metrics
Binary Fraud Classifier: Metrics, Thresholding, Calibration, and Online Evaluation You inherit a binary fraud classifier used to decide whether to blo...
Predict job changes month by month
Predict Monthly Job-Change Risk (Discrete-Time Survival Setup) Context You are building a monthly model to predict the probability that a LinkedIn mem...