Welcome to your interview.
The question is on your right: Choosing Features for Predictive Models. Take a moment with it first.
Talk your thinking through with me if you like - when you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes). Discussion and graded submissions share your five interviewer interactions, so spend them well.
You're training a supervised learning model and have many possible inputs, including raw fields, derived variables, and historical aggregates. You want a principled way to decide which features belong in the model.
How would you choose features for a predictive model?