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."
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...
Estimate OLS via streaming sufficient statistics
Streaming OLS and Ridge for Out-of-Core, High-Dimensional Linear Regression You need to estimate linear regression coefficients when the dataset is to...
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...
Choose and compute recommender evaluation metrics
Restaurant Recommender: Offline Evaluation and Modeling Context: You are scoring p(y=1|x) with logistic regression to predict if a user will engage wi...
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...
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...
Extract companies from noisy text
Extracting Company Names from Noisy Resumes and Web Snippets Context You receive messy resume text (PDF-to-text/OCR, varying casing) and scraped web s...
Design and evaluate an uplift model
Targeting a 20% Subset With a Free-Delivery Promotion to Maximize Incremental Orders per Dollar Context You work on a two-sided delivery marketplace a...
Build a robust ML pipeline
You inherit an ML pipeline that predicts next-7-day churn for users, but data quality is inconsistent and feature drift is suspected. A) Propose an en...
Design a model for subscription adoption prediction
Predicting 60-Day Adoption of Subscription by Non-Subscription Merchants Context You need to predict which merchants who are not currently using the S...
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Design a restaurant recommender under constraints
This question evaluates a candidate's competency in designing scalable machine learning recommender systems, covering retrieval and ranking architectu...
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 ...
Forecast response-rate trends with backtesting
This question evaluates proficiency in time-series forecasting and model validation, including feature engineering, model selection, rolling-origin ba...
Compare CNN/RNN/LSTM and implement K-means
Deep Learning Concepts and K-means Implementation (Onsite ML Interview) This is a two-part onsite round for a Data Scientist role: a conceptual deep-l...
Handle multicollinearity in feature selection
You are building an interpretable predictive model for an insurance company, such as a linear or logistic regression model for claim risk. Several inp...
Verify Machine-Learning Fundamentals for E-commerce Recommendation Platform
Verify Machine-Learning Fundamentals for E-commerce Recommendation Platform Rapid ML Fundamentals Check — Recommender Systems Context You are intervie...
Design Real-Time Fraud Detection with XGBoost Model
Design Real-Time Fraud Detection with XGBoost Model Real-Time Fraud Detection with XGBoost (Subscription Payments) Scenario You need to build and oper...
Train GradientBoostingClassifier with 5-Fold Cross-Validation
Train GradientBoostingClassifier with 5-Fold Cross-Validation Final Model Training: GradientBoostingClassifier with 5-Fold CV Context Assume the noteb...
Evaluate and Experiment with Harmful Content Detection Model
Evaluate and Experiment with Harmful Content Detection Model Evaluating a Harmful-Content Detection Model: Offline and Online Context You are given a ...