Your question is Prioritizing ML Features. 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).
How do you prioritize features for a machine learning model?
Explain a practical workflow for deciding which features to retain, transform, or remove. Address leakage, redundancy, missingness, interpretability, computational cost, and how you would validate that feature prioritization improves generalization rather than fitting noise.