These are Machine Learning questions of the kind Capgemini actually asks — the patterns that keep coming back in Capgemini's AI project interviews, where what matters is applying Machine Learning sensibly for clients rather than research depth. 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.
Capgemini Machine Learning concept questions
What is cross-validation and why would you use it instead of a single train-test split?
Asked in
Explain K-Means clustering and how you would choose the number of clusters.
Asked in
Capgemini Machine Learning applied & hands-on questions
You built a model predicting which insurance claims need manual review. Explain to the operations head — who has no technical background — what it does, how confident they should be, and what could go wrong.
Asked in
How to use this page: Capgemini 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 Capgemini Machine Learning rounds test.