Google Data Scientist Machine Learning Interview Questions
Practice 28 real Machine Learning interview questions for Data Scientist roles at Google.
"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."
When do you use mixed-effects models
You are modeling a user outcome (e.g., watch time or retention) across many countries and many users. Observations are nested (multiple days per user;...
Model Soccer Shot Conversion
This question evaluates probabilistic predictive modeling, spatial-temporal feature engineering, model calibration and evaluation, and identification ...
Explain logistic regression vs forests and boosting
Technical Screen — Machine Learning Answer all parts precisely. 1) Binary logistic regression: model, loss, gradient, convexity - Define the model: p(...
Explain linear regression to non‑technical stakeholders
This question evaluates understanding of linear regression fundamentals and related competencies, including defining target, features, coefficients, i...
Build and evaluate illegal-video classifier
This question evaluates competency in end-to-end Machine Learning system design, including multimodal modeling (vision, audio, text), data engineering...
Diagnose and fix flawed model fit
This question evaluates a data scientist's competency in applied supervised learning diagnostics, including feature encoding, feature scaling, class i...
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 ...
Predict and act on contract renewal risk
Predicting Enterprise Contract Renewal After a Quality Incident Context A video-conferencing provider experienced a spike in call disconnects. You nee...
Build and evaluate bad-link classifier
You have 1,000 URLs labeled as bad or good and a much larger unlabeled pool, with bad links rare. Design features and train a logistic regression. Exp...
Handle p≈n linear regression with L1
This question evaluates competence in high-dimensional linear regression, penalized estimation (L1/L2/elastic net), preprocessing and feature handling...
Handle highly imbalanced classification data
You must build a binary classifier for fraud with a 0.2% positive rate and 10M rows × 500 features. Propose an end-to-end plan that covers: 1) data sp...
Model Shot Success by Location
This question evaluates a candidate's skill in spatial probabilistic modeling, feature engineering, calibration, uncertainty quantification, and handl...
Build and evaluate a full ML pipeline
You must predict both (1) probability that a user will spend >$0 in the next 7 days (classification) and (2) expected spend in the next 7 days (regres...
Build Classifier: Evaluate with AUROC for Imbalanced Data
Detecting Dead Links: Build and Evaluate a Classifier You have a dataset of 1,000 URLs labeled as good, meaning alive, or bad, meaning dead. The class...
Decide between two vendors under constraints
You have two third‑party search vendors, A and B, plus historical order‑level data: lead_time_days, unit_price, on_time_rate, defect_rate, min_order_q...
Explain a favorite model end-to-end
Predictive Model Deep-Dive (End-to-End) Pick one predictive model you know deeply (e.g., logistic regression, gradient-boosted trees, transformer clas...
Build Model to Predict Customer Contract Renewal
Build a Model to Predict Customer Contract Renewal You are designing a model to predict whether an enterprise customer will renew a Google Meet contra...
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...
Detect Overfitting or Underfitting in Logistic Regression Models
Detect Overfitting or Underfitting in Logistic Regression Models Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction Scenario You are...
Address Overfitting with L1 Regularization in Regression
Linear Regression with Many Predictors and Few Observations You fit an ordinary least squares linear regression with 500 predictors and 600 observatio...