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."
Derive correlation bounds and omitted-variable bias
This question evaluates understanding of multivariate correlation structure and linear regression properties, focusing on feasible ranges and construc...
Explain fraud types and evaluate a fraud model
You are interviewing for a Fraud Data Scientist role at PayPal. Answer the following: 1) List common fraud types relevant to payments (e.g., account t...
Explain PCA and L2 Normalization in Machine Learning
Explain PCA and L2 Normalization in Machine Learning Scenario Experian DataLabs Data Scientist technical screen — a machine-learning deep-dive on the ...
Evaluate RAG System Accuracy and Cost Control Strategies
Evaluate RAG System Accuracy and Cost Control Strategies Technical Phone Screen: LLM Pipelines, Knowledge Graphs, and RAG Context You are designing an...
Improve Model Generalization with Cross-Validation and Feature Engineering
Improve Model Generalization with Cross-Validation and Feature Engineering Predict Next-Month Orders: Train/Test Split, Pipeline, and AUC Context You ...
Evaluate Models for Credit-Risk Scoring at Capital One
Evaluate Models for Credit-Risk Scoring at Capital One Scenario You are building a production-grade credit-risk scoring model (predicting probability ...
Fermi Estimation with Confidence Intervals: How Many Houses Does One Year of US Netflix Spending Buy?
Estimate how many houses could be purchased with the total amount that people in the United States spend on Netflix subscriptions in one year. You sta...
Explain key ML/stats concepts
Explain key ML/stats concepts You are taking an ML/Stats screening with conceptual multiple-choice questions. Answer the following: 1. CNN vs. RNN ...
Train with imbalanced sampled data
You are training a binary classifier on a very large dataset where the positive class is rare. Because the full dataset is too large to train on direc...
Adjust YouTube Ad Scores Using Mixed-Effects Linear Regression
Adjusting YouTube Ad Scores with Mixed-effects Regression One hundred reviewers each rate the same 100 YouTube ads on a 1 to 10 scale. Some reviewers ...
Compare Logistic Regression and Random Forest in Limited Data Scenarios
Compare Logistic Regression and Random Forest in Limited Data Scenarios You are designing a binary classifier with limited labeled data. The signal ma...
Implement Batch Gradient Descent for Linear Regression
Batch Gradient Descent for Linear Regression You are building a linear regression model from scratch and will optimize the parameters using batch grad...
How would you design a Shop Ads ranking algorithm?
This question evaluates a candidate's understanding of machine learning-driven ad ranking, auction mechanics, multi-stakeholder objective formulation,...
Design a hierarchical forecast for transactions
This question evaluates skills in hierarchical time-series forecasting, covering model selection and reconciliation, cross-validation design, intermit...
Explain random forests, bagging, and evaluation
This question evaluates understanding of ensemble learning and model evaluation, covering Random Forest aggregation, feature subsampling, bagging vers...
Design city home-price prediction system
End-to-End System Design: Predict Residential Property Sale Prices Context You are tasked with building a production-grade machine learning system to ...
Explain an ML project end-to-end with tradeoffs
Pick one of your production ML projects and walk through it end-to-end. Be specific: 1) Problem framing (prediction vs causal decisioning), target def...
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 ...
Explain your ML project end-to-end
End-to-End ML Project Deep Dive (7 Parts) Assume you are describing the most complex ML project on your resume. Answer each part precisely and concret...
Design a battery-life predictor and cold-start strategy
Smartphone Time-to-Empty (TTE) Prediction — Baseline, Features, Cold Start, Evaluation, and Monitoring Context You are building a per-device predictor...