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."
Choose ML metrics under asymmetric costs
This question evaluates a data scientist's competency in cost-sensitive binary classification, covering skills such as defining business cost matrices...
Design classification under missingness and imbalance
30-Day Readmission Classifier: End-to-End Plan Context: You are building a binary classifier to predict 30-day readmission using claims and EHR featur...
Explain your ML project end-to-end
End-to-End ML Project Deep Dive (7 Parts) Assume you are describing the most complex ML project on your resume. Answer each part precisely and concret...
Contrast Lasso vs Ridge trade‑offs
This question evaluates a Data Scientist's understanding of regularization methods (L1/Lasso, L2/Ridge, Elastic Net), their bias–variance trade-offs, ...
Design an end-to-end spam detection system
Design an End-to-End Email Spam Detection System You are asked to design a production-grade email spam detection system that meets the following const...
Explain MSE vs MAE, AUC, and imbalance handling
ML interview: losses, metrics, class imbalance, and thresholding Answer all parts concisely and precisely. 1) MAE vs. MSE in regression When would you...
Build and deploy an uplift targeting model
This question evaluates a candidate's ability to design and deploy uplift/causal targeting models, covering causal inference, uplift estimation, pre-t...
Implement PAVA spend-smoothing under no-borrowing constraint
Monotone Spending Plan via Isotonic L2 Regression (No-Borrowing) Context: You observe yearly discretionary income profit[1..65] (nonnegative reals) an...
Evaluate and monitor a credit risk model
Credit-Risk PD Model: Evaluation Priorities and End-to-End Plan Context: You are deploying a consumer credit probability-of-default (PD) model for 12-...
Apply Double ML with text-address features
Estimate the ATE of a First Reminder on CSAT via Double Machine Learning (DML) Context You have observational data on customer satisfaction (CSAT) sur...
Tune fraud threshold under review capacity and costs
Fraud Triage Thresholding with Calibrated Scores Context You have a fraud model that outputs a calibrated score s ∈ [0, 1] per account, where s ≈ P(fa...
Contrast LSTM and Transformer for long sequences
This question evaluates understanding of sequence-model architectures and system-level trade-offs for long-context autoregressive language models, cov...
Design a restaurant recommender under cold start
Design a Multi-Objective Restaurant Ranking System You own the restaurant recommendation surface for a city app. The goal is to rank nearby restaurant...
Design a System to Recommend Local Restaurant Profiles
Design a System to Recommend Local Restaurant Profiles Recommending Local Restaurant Pages in the News Feed Context Design a non-ads recommendation sy...
Design Comprehensive Recommendation System for Spokeo Features
Design Comprehensive Recommendation System for Spokeo Features Design an End-to-End Recommendation System for Spokeo Scenario You are designing a new ...
Choose Between Fine-Tuning and RAG for Client Chatbot
Choose Between Fine-Tuning and RAG for Client Chatbot Scenario You are building a client-facing chatbot that must answer questions grounded in the cli...
Scale and Normalize: When to Use Each Method?
Scale and Normalize: When to Use Each Method? Feature Scaling Before Modeling (CodeSignal Notebook) Context You're preparing features in a notebook st...
Detect Overfitting or Underfitting in Logistic Regression Models
Detect Overfitting or Underfitting in Logistic Regression Models Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction Scenario You are...
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...