Data Scientist Machine Learning Interview Questions
Practice 434 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."
Choose clustering vs regression; explain KNN
When would you use clustering vs. regression on a business problem with partially labeled outcomes? Specify the decision criteria (label availability,...
Design a hashtag recommender for News Feed
Design: Hashtag Recommendations in the News Feed Context You are adding hashtag recommendations alongside posts in a large social app’s News Feed. The...
Handle challenges in MMM/MMX
MMM Fragility Diagnosis and Remediation Plan (Weekly, 156 Weeks) Context You inherit a weekly Marketing Mix Model (MMM/MMX) with 156 weeks of data. Th...
Design recommendations objective balancing growth and monetization
Design a Multi-Objective Recommender for Long-Form Content You are designing the ranking objective and measurement plan for a long-form content recomm...
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....
Explain and tune XGBoost; prevent overfitting
XGBoost Tree Booster: Objective, Hyperparameters, Tuning for Imbalanced Detection, and Post-training Use Context: You are building a binary classifier...
Design a Cold-Start-Aware Recommender
System Design: Two-Stage Recommender for a New Content App Context You are designing recommendations for a new content app with sparse interactions an...
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 ...
Implement R² and Compare PCA With/Without Scaling
NumPy-only implementation: R² and PCA (Data Scientist take-home) Implement from scratch using only NumPy (no scikit-learn). Use float64 throughout and...
Explain a favorite model end-to-end
Predictive Model Deep-Dive (End-to-End) Pick one predictive model you know deeply (e.g., logistic regression, gradient-boosted trees, transformer clas...
Design real-time payments fraud model under constraints
Real-Time ML Policy Design: Prevent Unauthorized Purchases by Minors Context: You need to reduce unauthorized purchases by minors using their parents'...
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...
Explain SHAP vs VIF under collinearity
High Collinearity in Binary Classification: VIF, SHAP, and Interpretation Strategy You are modeling a binary outcome Y. Two numeric features A and B a...
Build a late-delivery risk model
Predict Late Delivery Risk at Order Creation Context You are given an anonymized dataset of marketplace orders with timestamps, store/customer/market ...
Choose optimal posted price under adverse selection
This question evaluates probabilistic reasoning, Bayesian updating, expected-value optimization under asymmetric information, and basic mechanism-desi...
Explain train-test generalization gap
This question evaluates understanding of model generalization and diagnostic competence within Machine Learning and Data Science, covering overfitting...
Normalize features and rank logistic coefficients
You are given a binary classification training dataset: - X: a 2D array of shape (n_samples, n_features) containing numeric features. - feature_names:...
Fit logistic regression and return top features
You are given: - X: a 2D numeric array where each row is a feature and each column is an observation (shape: n_features x n_samples). - feature_names:...
Explain factor leakage checks and IC/ICIR filtering
This question evaluates competency in factor-based predictive modeling, including detection of information leakage, use of information coefficient (IC...
Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors
Develop a Dynamic-Pricing Algorithm for Lyft You are tasked with building a dynamic-pricing system for Lyft, a two-sided ride-hailing marketplace. The...