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."
Identify and Fix Predictive Model Performance Gaps
Model Review: Month Encoding, Feature Scaling, and Imbalanced Data You are auditing an existing predictive model for operational performance. The curr...
Address Fraud Detection with Imbalance and Concept Drift Solutions
Address Fraud Detection with Imbalance and Concept Drift Solutions You are building a fraud-detection model for an online payments product that must s...
Address Overfitting in Supervised Learning Models
Address Overfitting in Supervised Learning Models You are evaluating a supervised learning model and observe that training performance is much better ...
Build Predictive Model for Product Metric: Steps Explained
Build a Predictive Model for a Product Metric You are interviewing for a data scientist role and are asked to design a predictive model for a key prod...
Predict Customer Churn with Machine Learning Workflow
Predict Monthly Customer Churn With an End-to-End ML Workflow A subscription platform wants to predict whether a customer will churn in the next month...
Analyze duplicating data in linear regression
This question evaluates understanding of linear regression theory and statistical inference, specifically how duplicating observations affects OLS coe...
Pricing a Mystery Money Box: Expected Value, Adverse Selection, and Competition
A company manufactures sealed boxes. Each box contains a cash amount that is an integer number of dollars drawn uniformly at random from $1$ to $100$ ...
When prioritize precision vs recall
Context You are working on a product team and building (or evaluating) a binary classifier that triggers an action (e.g., show a warning, block conten...
Explain Chunking for Financial RAG
Suppose you are building a retrieval-augmented generation (RAG) assistant over long financial research reports, filings, and policy documents. Explain...
What features and feature selection would you use?
Context You are building an ML system to rank/promote shop ads in an e-commerce feed/search page. At serving time, the system may score candidate shop...
Analyze expectations, correlations, and investment strategies
This multipart Machine Learning question evaluates probabilistic expectation and stopping-time reasoning, combinatorial expectations in random permuta...
Explain Linear Regression Feature Transformation Equivalence
Explain Linear Regression Feature Transformation Equivalence Linear Regression Feature Representations and High-Dimensional Modelling Context You are ...
Explain Deep Learning to a 5-Year-Old Child
Explain Deep Learning to a 5-Year-Old Child Microsoft Phone-Screen: Machine Learning Fundamentals You are interviewing for a machine learning/data sci...
Describe Your Machine Learning Project Experience
Describe Your Machine Learning Project Experience Machine Learning Experience: Walk Through a Project Context You are interviewing for a Data Scientis...
Design a Churn Model: Handle Missing Data and Justify
Design a Churn Model: Handle Missing Data and Justify Churn Prediction on Messy Subscription Data Context You are building a binary churn-prediction m...
Explain Overfitting and Underfitting in Machine Learning
Explain Overfitting and Underfitting in Machine Learning ML Fundamentals and Computer Vision: Core Concepts Instructions You are interviewing for a da...
Evaluate Fake-Account Classifier with Precision and Recall Metrics
Evaluate Fake-Account Classifier with Precision and Recall Metrics Evaluating a Fake-Account Classifier in Production Scenario You have trained a mode...
Evaluate Classifier with Precision, Recall, and Fairness Metrics
Evaluate Classifier with Precision, Recall, and Fairness Metrics Offline Evaluation Framework for a Harmful-Content Video Classifier Context You are e...
Choose Models for Imbalanced Data and Time-Series Forecasting
Choose Models for Imbalanced Data and Time-Series Forecasting Scenario You must choose and tune models for (a) forecasting marketplace demand with sea...
Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability
Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability Large-Scale Recommendation System: Ensembles, Overfitting, Metrics, Architectu...