These are Machine Learning questions of the kind Amazon actually asks — the patterns reported from Amazon's AI-engineering rounds, where design trade-offs, scale and failure modes matter as much as definitions. Treat this page as a mock interview: say every answer out loud before revealing it. If one surprises you, the lesson behind it is linked at the bottom.
Amazon Machine Learning concept questions
What is data leakage, how does it happen, and how do you prevent it?
Asked in
Amazon Machine Learning applied & hands-on questions
Design an ML system that predicts delivery time for a food-delivery app and shows it to the customer at order time. Cover framing, features, metric and deployment.
Asked in
A team reports 99.2% test accuracy on predicting which customers will upgrade their subscription. They want to ship. What do you say?
Asked in
How to use this page: Amazon rarely asks something you've never seen — they ask a standard Machine Learning concept and then push one level deeper ("why?", "what would you do if..."). Master the concept in the Machine Learning course lessons, and the follow-up stops being scary.
Keep practising: Supervised vs Unsupervised, Overfitting & Underfitting, Evaluation Metrics and ML Project Lifecycle cover what most Amazon Machine Learning rounds test.