Your question is Handling 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 working on a classification problem where one class is much rarer than the other, and the team is concerned that standard evaluation may hide poor minority class performance. You need to explain how you would evaluate and handle this kind of dataset during model development.
How do you handle imbalanced datasets in machine learning?