Your question is Feature Selection for ML Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are working on a supervised learning model and the feature set is large, mixed quality, and partly redundant. Some variables may be noisy, correlated, or only useful in combination with others.
How would you approach feature selection in a machine learning model?