These are Machine Learning questions of the kind Accenture actually asks — the patterns that keep coming back in Accenture'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.
Accenture Machine Learning concept questions
Why is accuracy often a bad metric, and what would you use instead?
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A client says their model worked well for six months and has now got worse, with no code changes. What is happening?
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Accenture Machine Learning applied & hands-on questions
A retail client asks for a fixed-price proposal to 'use AI to reduce stockouts'. Write the scoping questions you would ask and the phased structure you would propose.
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How to use this page: Accenture 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 Accenture Machine Learning rounds test — and the Accenture exam guide covers the rest of their selection process.