Your question is Handling Class Imbalance in 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're training a supervised classification model, but the positive class is much rarer than the negative class. You want the model to detect the minority class reliably without being misled by accuracy.
How do you address class imbalance in a dataset when training a predictive model?