Your question is Address Model Overfitting. 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've trained a model that looks strong on the training set, but the gap to validation or real-world performance suggests it is memorizing patterns instead of generalizing. Your team wants to know how you would systematically diagnose and reduce the overfitting.
How would you approach solving a problem where your model overfits to the training data?