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 evaluation metrics for imbalanced risk model
This question evaluates understanding of cost-sensitive decision making, probabilistic thresholding, evaluation metric selection under class imbalance...
Optimize precision–recall under class imbalance
You have extreme class imbalance (positive rate ~1%). You score 12 examples as follows (id, true_label, score): A,1,0.92; B,0,0.90; C,0,0.88; D,0,0.70...
Evaluate and select K in K-means
K-means Clustering: Concepts, Initialization, Model Selection, Preprocessing, and Business Validation Context: You are clustering customer data with n...
Explain SVM kernels and complexity
This question evaluates understanding of Support Vector Machines, including support vectors and primal/dual formulations, the kernel trick and why Gra...
Contrast L1 and L2 regularization effects
Ridge (L2) vs Lasso (L1) in Linear and Logistic Regression Context: You are comparing L2 (Ridge) and L1 (Lasso) regularization for linear and logistic...
Design end-to-end regression for energy demand
End-to-End Daily Energy Prediction for Commercial Buildings Context You are asked to design and justify an end-to-end regression system that predicts ...
Design enterprise file recommendations under ACLs
This question evaluates a Data Scientist's ability to design production-grade machine learning and recommender systems for enterprise file suggestions...
Model preference without ground truth
This question evaluates a data scientist's competency in uplift modeling, causal inference, experimental design, weak supervision, and bias and shift ...
Choose metrics for fake-user classifier
Classifying Fake Accounts: Metrics, Capacity, Thresholding, and Validation Context - Population: 10,000,000 daily active users (DAU) - True fake rate ...
Build a Bayes classifier for reviewer types
This question evaluates Bayesian inference skills, including posterior updating under conditional independence, likelihood modeling for categorical ob...
Validate and monitor ranking model end-to-end
Expedia Hotel-Ranking Model: Evaluation, Metrics, Diagnostics, Rollout, and KPI Alignment Context: You are building a learning-to-rank (LTR) model to ...
Detect clickbait without labels, then supervise
Detecting Clickbait Ads Without Labeled Data Context You are asked to detect clickbait ad creatives when there is no labeled training data. You have i...
Design a hybrid marketplace fraud system
This question evaluates competency in designing hybrid fraud detection systems, covering feature engineering across text, image, graph and telemetry s...
Design fraud detection across channels with unknowns
This question evaluates a data scientist's competence in designing and operationalizing multi-channel fraud detection systems, covering cost-sensitive...
Build a real-time ATO model
This question evaluates a candidate's competency in designing low-latency, production-grade real-time machine learning systems for account-takeover de...
Implement robust k-means from scratch
Implement K-Means Clustering From Scratch (Production-Ready) Context You are asked to implement K-Means clustering from scratch for a machine learning...
Compare CNN, RNN, and LSTM rigorously
Sequence Modeling: Rigorous Comparison of CNNs, RNNs, and LSTMs Context and assumptions: - We are modeling 1D sequences of shape (batch=32, time=100, ...
Compare bagging vs boosting on imbalanced data
Fraud Detection on 10M Time-Ordered Transactions (0.5% Fraud) You are building a binary classifier to detect 0.5% fraudulent events among 10,000,000 t...
Implement random forest with OOB and imbalance
Implement a Memory-Efficient Random Forest (Binary Classification) Under Constraints You are asked to design and implement a Random Forest for binary ...
Answer basic probability and statistics questions
This question evaluates core probability and statistics competencies, including interpretation of Poisson rate parameters from PMF shapes, identificat...