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."
Scale and Normalize: When to Use Each Method?
Feature Scaling Before Modeling (CodeSignal Notebook) Context You're preparing features in a notebook step before training a model. You have a pandas ...
Optimize Surge Notifications for Rideshare Drivers
Scenario A rideshare marketplace experiences airport demand spikes. When demand exceeds supply, the system can send surge-pricing push notifications t...
Identify Unsupervised Techniques for Detecting Fraudulent Transactions
Unsupervised Fraud Detection: Modeling and Evaluation Without Labels Scenario You receive millions of historical transactions with no fraud labels. Ma...
Choose optimal posted price under adverse selection
You are negotiating to buy an item whose true quality is unknown to you. - With probability 0.7, the item is defective and would be worth $7,000 to yo...
Build a model to predict wine quality
Modeling task: Predict wine quality from a CSV You are given a clean CSV dataset about red wine. The target (dependent) variable is: - quality (intege...
Derive expected inversions and mean distribution
Random permutation inversion statistics Let π be a uniformly random permutation of length N. Let X be the number of inversions in π. 1. Compute the ex...
How would you predict a car’s turning intention?
At an intersection, there are n vehicles stopped or approaching. For each vehicle, you have a short history (e.g., last 3–10 seconds at 10 Hz) of: - P...
Handle Missing Values and Choose ML Algorithms Wisely
ML Interview: Core Modeling Concepts Context: Technical phone screen for a Data Scientist role. Assume primarily tabular datasets; address both classi...
Compare Regularization Techniques and Their Use Cases
Technical Phone Screen: Model Evaluation, Regularization, and Regression Basics Instructions Answer the following, focusing on clarity and practical i...
Construct a Churn-Prediction Pipeline Using Scikit-Learn
Construct a Churn-Prediction Pipeline in scikit-learn Scenario You are a data scientist on a subscription business. You need to build a model that pre...
Derive and regularize logistic regression
Churn Propensity with Logistic Regression: Theory, Validation, and Decisions Context: You are building a churn propensity model (y ∈ {0,1}) using logi...
Diagnose and fix underperforming ML model
Rapidly Improving Recall Under Class Imbalance (One-Day Plan) Context You inherit a binary fraud detection model with severe class imbalance (positive...
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...
Explain linear regression to non‑technical stakeholders
Explain linear regression to a non-technical executive using a concrete business example (e.g., predicting weekly sales from price, ad spend, and stor...
Explain Random Forest randomness and implications
Random Forest — Rigor and Practical Choices Context: You are building a binary classifier with a Random Forest. The dataset has 100,000 rows, 100 feat...
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: ...
Design enterprise file recommendations under ACLs
Design a system to recommend to a signed-in enterprise user the next files they are most likely to open in a productivity suite. Cover: (1) key signal...
Contrast Lasso vs Ridge trade‑offs
Regularization choices for modeling contribution per order (p=50) Context: You are building a linear model for contribution per order (continuous outc...
Diagnose and fix flawed model fit
Fixing a Churn Classifier: Encoding, Imbalance, Evaluation, and Fairness Context You inherit a binary classifier that predicts churn=1. The current im...
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...