Machine Learning Interview Questions
Practice 639 real Machine Learning interview questions for 2026 — Machine Learning interview questions drawn from Amazon, Meta, Google, TikTok, and Capital One, with real questions from actual interviews and detailed solutions. This collection is built for interview preparation focused on production-ready ML: expect questions that test modeling and mathematics, coding in Python, ML system design, MLOps and deployment, and modern GenAI topics such as transformer fundamentals, embeddings, and retrieval-augmented generation. Companies emphasize reliability, data quality, and end-to-end ownership as much as algorithmic chops. What’s distinctive: interviews now blend theory, coding, and system thinking — you’ll be evaluated on algorithmic intuition, experiment design and metrics, feature and data engineering, model monitoring and drift detection, and cost/reliability tradeoffs for serving models at scale. To prepare, strengthen fundamentals (linear models, trees, probabilistic reasoning), implement end-to-end projects, rehearse ML system-design case studies, and run mock interviews that combine coding, math, and production scenarios.
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"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."
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"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."
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"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."
How would you manage precision/recall for fraud detection?
Scenario You own (or significantly contribute to) a production fraud detection system that flags transactions/users as fraud vs legit. - The model out...
Compare Random Forests and Boosted Trees: Bias, Variance, Speed
Compare Random Forests and Gradient-Boosted Trees You are choosing and configuring tree-based ensemble models for a product-facing data-science proble...
Compare Logistic Regression and Random Forest in Limited Data Scenarios
Compare Logistic Regression and Random Forest in Limited Data Scenarios You are designing a binary classifier with limited labeled data. The signal ma...
How to Analyze and Model Behavioral Data Effectively?
Analyze and Model Behavioral Data Effectively You receive a raw event-level behavioral dataset for a product funnel. The interviewer asks you to clean...
Optimize Hyper-parameter Search to Prevent Combinatorial Explosion
Enumerating Grid Search and Avoiding Hyperparameter Explosion You are building a hyperparameter optimization service that must enumerate every grid-se...
Explain modern modeling and alignment methods
In a machine learning technical interview, explain the following topics in depth. For each one, describe the problem it solves, the core idea, key tra...
Reduce LLM hallucination and handle class imbalance
Answer the following applied ML/LLM questions. 1) LLM hallucination & token cost control You are building a chatbot over an internal knowledge base. 1...
How would you design Shop-ad ranking?
Suppose the previous experiment shows that, in some contexts, users are more likely to convert when shown an ad that leads to an in-app Shop rather th...
Propose an ads recommendation model for shop ads
You need to propose a modeling approach for recommending/ranking shop ads (i.e., which shop ads to show and in what order) for a marketplace app. Desc...
Build and evaluate donation propensity model
You need a model to maximize expected net revenue from solicitations. Costs: online reach costs $1 per person; gala attendance costs $100 per attendee...
Build a model to infer home vs office vs public
You must infer whether a Facebook session’s network context is home, office, or public venue to inform Portal targeting. Constraints: IPs may be share...
Estimate b when features exceed samples
Consider the linear model y = Xb + ε with X ∈ R^{n×(m+1)} including an intercept. a) Derive the OLS estimator b̂ = (XᵀX)^{-1}Xᵀy, stating the rank con...
Handle p≈n linear regression with L1
You must fit linear regression with p = 500 predictors and n = 600 observations. What failure modes do you expect and why does OLS overfit when p is c...
Choose evaluation metrics for imbalanced risk model
Cost-Sensitive Fraud Detection: Thresholding, Metrics, and Calibration Assume a binary fraud classifier outputs calibrated probabilities p = P(y=1|x)....
Choose regularization norms and model formulations
Regularization and model choice. 1) For linear and logistic regression, write the objective functions with L0, L1, L2, and L-infinity penalties in bot...
Detect leakage and evaluate a prediction model
Churn Prediction Model: Leakage, Validation, KPIs, Interpretation, Monitoring Context: You inherit a weekly-scored model that predicts whether a user ...
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...
Compare trees, RF, and gradient boosting
Decision Trees, Random Forests, and Gradient-Boosted Trees You are interviewing for a Data Scientist role and are asked to compare common tree-based m...
Explain an ML project end-to-end with tradeoffs
Pick one of your production ML projects and walk through it end-to-end. Be specific: 1) Problem framing (prediction vs causal decisioning), target def...
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 ...