These are Machine Learning questions of the kind Cognizant actually asks — the patterns that keep coming back in Cognizant'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.
Cognizant Machine Learning concept questions
What is the difference between classification and regression? Give examples.
Asked in
What is feature scaling, when is it needed, and when is it not?
Asked in
Cognizant Machine Learning applied & hands-on questions
A colleague shares this summary of a customer-default model. List everything you would question before it goes anywhere near production.
Asked in
Model: Random Forest, 500 trees, max_depth=None
Dataset: 42,000 customers, 68 features
Train/test split: random 80/20
Accuracy on test set: 96.4%
Top features: days_since_last_payment, account_status_code,
collection_agent_assigned, income, city_encoded
Recommendation: deploy next sprintHow to use this page: Cognizant 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 Cognizant Machine Learning rounds test — and the Cognizant questions section covers the rest of their selection process.