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."
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...
Compare trees, RF, and gradient boosting
Decision Trees, Random Forests, and Gradient-Boosted Trees You are interviewing for a Data Scientist role and are asked to compare common tree-based m...
Write and explain gradient descent pseudocode
Task: Batch Gradient Descent for Linear Regression (with Intercept) You are interviewing for a Data Scientist role and are asked to implement batch gr...
Build and evaluate airline delay prediction model
You are given several CSVs for the classic airline delay challenge with columns like flight_date, carrier, flight_num, origin, dest, sched_dep, sched_...
Choose and justify ML algorithms for tabular prediction
You must choose an algorithm for tabular prediction of arrival delay under these constraints: 500k rows, 120 features (mixed numeric/categorical with ...
Improve low R² without p‑hacking
Predicting Contribution per Order with Low R² Context You are modeling contribution per order (a continuous per-order outcome such as margin or profit...
Build predictive model for feature rollout targeting
Before global launch, you want to predict which users or products would benefit most from the 'More like this' button so you can stage rollout. Design...
Build a package-allocation model for couriers
Automatic Package-to-Courier Assignment with ML + Optimization You previously assigned packages to couriers manually. Design an end-to-end system that...
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 ...
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 a target‑user prediction system
Predicting 30‑Day Adoption of Product P for Budgeted Outreach Context You are tasked with building a model to prioritize user outreach for Product P. ...
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 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 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...
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...
Define scalable train/validation for churn
This question evaluates a data scientist's competency in designing scalable, leakage-safe training, validation, evaluation, and serving pipelines for ...
Explain CNN shapes, params, and trade-offs
This question evaluates understanding of convolutional neural network tensor shapes, parameter counts and multiply–accumulate (MAC) calculations, rece...
Derive and compare core ML and RL methods
ML Fundamentals Technical Screen — Multi‑part Question Context: You are given a set of core machine learning topics to address rigorously. For each pa...
Explain core ML concepts and metrics
You are interviewing for a Data Scientist role. Answer the following ML fundamentals questions clearly and concisely. Concepts 1. Explain the bias–var...