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."
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...
Build and assess CTR prediction
CTR Prediction with Delayed Feedback and Extreme Class Imbalance You are building a model to predict the probability that an ad impression results in ...
Design and critique an abuse-detection ML system
This question evaluates system-design and production machine learning competencies including large-scale classification versus risk scoring, handling ...
Explain linear regression and Transformer fundamentals
This question evaluates core competencies in statistical modeling and deep learning architecture, specifically linear regression (optimization objecti...
Diagnose Bias and Variance Across KNN and Regularized Models
Sharpen your ability to diagnose bias and variance from learning curves, explain how KNN changes with neighborhood size, and compare L1, L2, and elast...
Explain KNN and PCA and key tradeoffs
In a Data Scientist internship interview, you are asked ML fundamentals: 1) K-Nearest Neighbors (KNN) - Explain how KNN works for classification and r...
Design a house-price prediction model
This question evaluates machine learning and data science competencies including regression model design, feature engineering, data-splitting and leak...
Explain Ensemble Learning and Its Main Families
Explain Ensemble Learning and Its Main Families Explain why combining multiple models can improve predictions. Compare bagging, random forests, boosti...
Diagnose Multicollinearity in Flight Delay Prediction Model
Diagnose Multicollinearity in a Flight Delay Prediction Model You are building a model that predicts whether a flight will be delayed using historical...
How would you choose a classification threshold?
You trained a binary classifier that outputs a probability score p(y=1|x). You must choose a decision threshold t to convert probabilities into class ...
Pick a Number 1-100 Where the Higher Pick Pays a 10-Point Penalty: Optimal Strategy
Two players simultaneously each pick an integer from 1 to 100 (inclusive). The winner is decided as follows: take the larger of the two picks, subtrac...
Build leak-safe sklearn model with calibration
You must build an end‑to‑end scikit‑learn pipeline to predict churn_28d at decision time t0 using only features available at or before t0 (no leakage)...
Diagnose outliers and influence in linear regression
OLS Diagnostics: Outliers, Leverage, Influence, and Cook's Distance Context You are fitting an ordinary least squares (OLS) linear regression with an ...
Build and evaluate a conversion prediction model
Predicting 7-Day Purchase After Email Send Context You are given a CSV where each row is a user–email send (or scheduled send/control), with columns: ...
Build and evaluate imbalanced binary classifier
Take‑home: Imbalanced Binary Classification with Temporal Split, Calibration, and Operating Point Selection Context You are given an event‑level datas...
Detect and suppress bad sellers robustly
This question evaluates a candidate's competence in designing end-to-end machine learning risk systems, including label strategy and triage, feature e...
Build and evaluate an order prediction model
This question evaluates a data scientist's competency in building and evaluating binary classification models with temporal constraints and operationa...
Build a predictive model from TurboTax sample data
Build a predictive model from TurboTax sample data evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practi...
Predict a Company's Stock Price from Six Years of Web Search Logs
You are given a DataFrame containing the past six years of web search logs. Each row is a single search event with the following columns: | Column | T...
Explain Medical AI Data and Evaluation
This question evaluates competence in medical conversational AI covering data sourcing and cleaning, leakage-aware dataset splitting, model choice (pr...