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."
Explain CNN shapes, params, and trade-offs
CNN Shapes, Compute, and Design Trade-offs Context You are given an input tensor X with shape H×W×C = 64×64×3. Consider the following convolutional ne...
How would you choose a classification threshold?
You trained a binary classifier that outputs a probability score p(y=1|x). You must choose a decision threshold t to convert probabilities into class ...
Design a lead-scoring model
Question You are interviewing for a Data Scientist role on a marketing/growth team. Sales has limited outreach capacity, so the business wants a lead-...
Design regression and classification ML pipelines
Take‑Home: Two End‑to‑End ML Workflows on Tabular Data Objective Design and implement two complete machine learning workflows on tabular data (typical...
Train with imbalanced sampled data
You are training a binary classifier on a very large dataset where the positive class is rare. Because the full dataset is too large to train on direc...
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...
Evaluate Models for Credit-Risk Scoring at Capital One
Scenario You are building a production-grade credit-risk scoring model (predicting probability of default within a fixed horizon) for Capital One. The...
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 ...
Differentiate Overfitting and Underfitting in Machine Learning
ML/DL Fundamentals for a Recommendation Engine Context You are preparing for a take-home assessment on ML/DL fundamentals relevant to building a recom...
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...
Design an ad recommendation ranking approach
You are designing an ad recommendation (ad ranking) system for a consumer app. Goal Maximize long-term business value while maintaining a good user ex...
Explain decision trees and tree ensembles
Prompt 1. Explain how a decision tree works for classification or regression. 2. How does the tree choose a split (objective functions for classificat...
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...
Construct a Churn-Prediction Pipeline Using Scikit-Learn
Churn Prediction Pipeline in scikit-learn Scenario You are building a churn prediction model for a subscription business. Churn is defined as whether ...
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...
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...
Choose threshold under asymmetric costs
You own a credit-card fraud classifier deployed as a probability scorer. Choose an operating threshold under asymmetric costs and justify it quantitat...
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...