Welcome to your interview.
The question is on your right: Feature Engineering for Sparse Data. 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 working on a supervised learning task where most features come from high-cardinality categorical fields and text-like signals, so the design matrix is very high-dimensional and mostly zeros. You need a practical way to create useful features without overfitting or making training too expensive.
How do you approach feature engineering for high-dimensional, sparse datasets?