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 training a binary classifier where the positive class is much rarer than the negative class. A model with high accuracy may still miss most of the cases you care about.
How would you handle imbalanced datasets?