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."
Choose regularization norms and model formulations
Regularization and model choice. 1) For linear and logistic regression, write the objective functions with L0, L1, L2, and L-infinity penalties in bot...
Design anomaly detection and handle imbalanced logistic regression
You receive a time‑stamped transactions dataset: columns [event_time (UTC), customer_id, merchant_id, amount, country, device_type, features...], labe...
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...
Design ML deployment with GitHub and Jenkins
Design an end‑to‑end ML deployment for a prediction model using GitHub and Jenkins: 1) Propose a repo layout (src/, features/, data_contracts/, tests/...
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...
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...
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...
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...
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...
Implement PAVA spend-smoothing under no-borrowing constraint
Monotone Spending Plan via Isotonic L2 Regression (No-Borrowing) Context: You observe yearly discretionary income profit[1..65] (nonnegative reals) an...
Validate and monitor ranking model end-to-end
Expedia Hotel-Ranking Model: Evaluation, Metrics, Diagnostics, Rollout, and KPI Alignment Context: You are building a learning-to-rank (LTR) model to ...
Compare Random Forests vs Gradient Boosting rigorously
Technical ML Choice: Random Forest vs. Gradient-Boosted Trees for Large-Scale Binary Classification Problem Setup You need to choose between a Random ...
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...
Choose linear regression or decision tree appropriately
Choose Between Linear Regression and a Decision Tree Under a Hinge and Interaction DGP Context You have 100,000 i.i.d. observations with features x1 (...
Design a restaurant recommender under cold start
Design a Multi-Objective Restaurant Ranking System You own the restaurant recommendation surface for a city app. The goal is to rank nearby restaurant...
Describe algorithm to find function maximum
Consider a real-valued, differentiable function f(x) defined on R (or more generally on R^n). You have access to an oracle that, for any input x, can ...
Leverage Existing Model for Low Credit Score Applicants
Expanding a Credit-Risk Model to a New Score Band Scenario Your current probability-of-default (PD) lending model was trained only on applicants with ...
Choose Between Fine-Tuning and RAG for Client Chatbot
Scenario You are building a client-facing chatbot that must answer questions grounded in the client's proprietary documents. You must choose how to im...
Design an ML Model for Interview Recommendation Pipeline
Scenario You are designing and deploying an ML model that mirrors a real-world recommendation pipeline serving a large product catalog with strict lat...