These are Machine Learning questions of the kind Microsoft actually asks — the patterns reported from Microsoft'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.
Microsoft Machine Learning concept questions
How do you decide which algorithm to use for a given problem?
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Microsoft Machine Learning applied & hands-on questions
Design a system that routes incoming support tickets to one of 12 teams. Cover the model, the evaluation, the rollout, and how you'd handle the cases it gets wrong.
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Diagnose each and give your first action: (A) train 99%, test 72%. (B) train 68%, test 67%. (C) train 91%, test 90%, but the business says it's useless. (D) train 100%, test 100%.
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How to use this page: Microsoft 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 Microsoft Machine Learning rounds test.