Data Scientist Machine Learning Interview Questions
Practice 411 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."
Design leakage-free predictive maintenance pipeline
Predict 24-hour Machine Faults from an Hourly Panel (End-to-End Design) Context You are given a machine–hour panel: one row per machine per hour with ...
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 ...
Choose ML metrics under asymmetric costs
Binary Classifier With Asymmetric Costs: Fraud vs. Cancer Context: You own a production binary classifier and must make product/ML decisions under asy...
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 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 MSE vs MAE, AUC, and imbalance handling
ML interview: losses, metrics, class imbalance, and thresholding Answer all parts concisely and precisely. 1) MAE vs. MSE in regression When would you...
Select the better $5 promo-targeting model
Coupon Targeting Under a Daily Budget: Policy, OPE, Calibration, and Monitoring Context - You have two user-scoring models for a $5 coupon: M0 (curren...
Design email to avoid Promotions without online tests
Offline Design of a Transactional Email to Minimize Promotions/Spam Classification Context You must finalize the design of a single in‑game transactio...
Detect and suppress bad sellers robustly
System Design: Identify and Suppress Bad Sellers in a Commerce Marketplace Context You are designing an ML-driven risk system for a large-scale market...
Design a hierarchical MF delinquency forecasting system
Forecasting 90+ Day Delinquency Rates for Multifamily Loans: Hierarchical, Leakage-Safe System Design Context You need to forecast 90+ day delinquency...
Evaluate and monitor a credit risk model
Credit-Risk PD Model: Evaluation Priorities and End-to-End Plan Context: You are deploying a consumer credit probability-of-default (PD) model for 12-...
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...
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...
Design a fintech homepage ranker
Personalized Product Ranking for a Fintech Home Page — End-to-End Design Context You are designing a personalized ranking system for a fintech app’s h...
Optimize IG Shopping ranking with multiple objectives
Instagram Shopping: Multi-Objective Ranking With Fairness, Fraud Robustness, and On-Device Constraints You are designing the Instagram Shopping home f...
Analyze overfitting, DenseNet, preprocessing, and cross-validation
Image Classification in Healthcare: End-to-End Interview Task Context: You are designing and evaluating an image-classification system for a healthcar...
Implement random forest with OOB and imbalance
Implement a Memory-Efficient Random Forest (Binary Classification) Under Constraints You are asked to design and implement a Random Forest for binary ...
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...
Explain key ML/stats concepts
You are taking an ML/Stats screening with conceptual multiple-choice questions. Answer the following: 1. CNN vs. RNN - What kinds of input structur...
Evaluate New-City Performance with Little Data
You have abundant labeled autonomous-driving data from Beijing and have already built an evaluation system there. Now the company wants to assess perf...