Your question is Handle Missing and Skewed 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).
You are working with a supervised learning dataset that has missing values and a few heavily skewed numeric features. The model will be used in production, so the preprocessing has to be fit correctly and carried forward at inference time.
How do you handle missing or highly skewed data in a dataset before feeding it into a predictive model?