Your question is Handle Imbalanced Classification Data. 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 given a classification problem where the positive class is rare, and a model that looks good on accuracy can still miss most of the cases that matter.
How would you handle imbalanced datasets in a classification problem?