Your question is Handle Highly Imbalanced Classification. 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 on a supervised classification problem where the positive class is rare, and a standard accuracy score would hide most of the mistakes. The team needs a model that can find the minority class without flooding operations with false alarms.
How would you handle a highly imbalanced dataset?