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."
Build and validate a binary classifier
ML Pipeline with Grouped CV, Imbalance Handling, Calibration, and Thresholding Context: You have a labeled dataset where the target is is_active_30d (...
Explain AUC, imbalance, losses, and networks
Imbalanced Classification & Regression: ROC/PR, Losses, and Training Strategies You are evaluating a binary classifier and a regression head in a mach...
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Diagnose and fix linear regression assumption breaks
OLS Assumptions, Diagnostics, Remedies, and Refitting Under Heteroskedasticity and Multicollinearity You are fitting a linear regression with Ordinary...
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 an uplift model for targeting
Flu-shot Campaign: Treatment-Effect Modeling and Targeting Policy You have historical campaign logs from last season that include randomized holdouts....
Design and validate a cost-sensitive classifier
Binary Purchase Prediction with Delayed Labels and Imbalanced Classes Context - Goal: Ship a real-time binary classifier that predicts whether a user ...
Design a News Feed with APIs
Personalized News Feed System Design (Push + Pull) Context You are designing a large-scale personalized news feed for a consumer application. The feed...
Design a production face recognition system
Design an On-Device Face Recognition System for Mobile Access Control Context You are designing a face-based access control system for mobile devices ...
Achieve 0.95 precision via thresholding
Deploying a High-Precision Classifier on an Imbalanced Dataset You are given a binary classification problem with 50,000 samples and ~5% positives. Th...
Build a leak-free sklearn pipeline
Take-home: Imbalanced Binary Classification Pipeline with scikit-learn You are training a binary classifier on tabular data with the following feature...
Design fraud detection across channels with unknowns
Fraud Detection Strategy for a Multi‑Channel Marketplace Context: You are designing a fraud detection system for a large marketplace operating across ...
Design real-time live-stream recommendations
Design a Real-Time Recommendation System for Live Streams Context: You are designing a recommender for a large live-streaming platform. Assume you hav...
Build a fair loan classifier
You are given a dataset of loan applicants. Each row represents one loan application and contains applicant attributes, loan attributes, and a binary ...
How do you choose a classification threshold?
Context You built a binary sentiment classification model (e.g., positive vs. negative) and need to deploy it in a product where actions depend on the...
Answer basic probability and statistics questions
You are given several short, independent probability and statistics questions similar to those in a data / ML screening test. Answer all sub-questions...
Compare XGBoost and LightGBM
In a machine learning interview, explain the key differences between XGBoost and LightGBM for a tabular booking-conversion prediction problem. Your an...
How to Analyze and Model Behavioral Data Effectively?
End-to-End Conversion Modeling on a Raw Behavioral Dataset Scenario You receive a raw, event-level behavioral dataset (e.g., user actions, sessions, m...
Evaluate Factors Before Replacing Recommendation Model
Ads Model Replacement: Evaluation, Trade-offs, Experimentation, and Executive Readout Scenario A large ads platform has built a new recommendation/ran...
Evaluate OutlierHandler Class for Code Quality and Testing
Code Review: OutlierHandler and Imputer Classes Context You are given a Python module that implements one OutlierHandler class and three Imputer class...