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."
How to Analyze and Model Behavioral Data Effectively?
End-to-End Conversion Modeling on a Raw Behavioral Dataset Scenario You receive a raw, event-level behavioral dataset (e.g., user actions, sessions, m...
Evaluate OutlierHandler Class for Code Quality and Testing
Code Review: OutlierHandler and Imputer Classes Context You are given a Python module that implements one OutlierHandler class and three Imputer class...
Evaluate New Model's Performance Against Existing System
Scenario You are evaluating a new machine-learning model that detects harmful content on a large consumer platform. Leadership needs evidence that the...
Compare RNNs and Transformers for Long-Sequence Text Classification
Scenario You are designing a long-sequence text classification system under tight inference latency constraints (e.g., large documents or logs that mu...
Design a Churn Model: Handle Missing Data and Justify
Churn Prediction on Messy Subscription Data Context You are building a binary churn-prediction model for a subscription product. Historical customer-l...
Detect Overfitting or Underfitting in Logistic Regression Models
Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction Scenario You are building a large‑scale binary classifier (e.g., click/conversion...
Design an Automated Home-Price Valuation Model
Scenario You are building an automated house-price valuation service for a real-estate platform. Question Design a home-price estimation system. Walk ...
Diagnose Bias–Variance Trade-off in Supervised Learning
Supervised Learning Review (Customer-Facing Ranking Context) You are designing and evaluating models for a customer-facing ranking service (e.g., orde...
Choose Models for Imbalanced Data and Time-Series Forecasting
Scenario You must choose and tune models for (a) forecasting marketplace demand with seasonality and trend, and (b) detecting fraud where the positive...
Evaluate and Experiment with Harmful Content Detection Model
Evaluating a Harmful-Content Detection Model: Offline and Online Context You are given a binary classification model that detects harmful content in a...
Explain Decision-Tree Training and Clustering Algorithms
Decision Trees and Clustering: Training Mechanics and Core Principles Context Technical/phone screen for an Applied Scientist/Data Scientist role, ass...
Train GradientBoostingClassifier with 5-Fold Cross-Validation
Final Model Training: GradientBoostingClassifier with 5-Fold CV Context Assume the notebook already contains a prepared feature matrix X and a binary ...
Evaluate K-Fold Cross-Validation for Model Selection
Model Selection and Validation for a New Feature Launch You are selecting and validating predictive models (supervised learning) for a new product fea...
Design a robust traffic forecasting pipeline
Forecasting Daily Amazon Retail Traffic: End-to-End Design You are given 5 years of daily Amazon retail site traffic counts. Design an end-to-end fore...
Present and defend your data challenge end-to-end
10–12 Minute Interviewer-Driven Walkthrough: Recent Data Challenge Provide a concise, structured walkthrough of a real project you led end-to-end. Ass...
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 missing data and outliers robustly
Customer Churn Modeling: Preprocessing, Missingness, Outliers, and Evaluation Context You are building a binary churn model for a consumer subscriptio...
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 ...
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 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...