Your question is Bias in Models and 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 reviewing a supervised learning pipeline and notice that model errors may come from both the data and the model itself. You want a structured way to diagnose whether the issue is underfitting, overfitting, or biased data collection.
How would you deal with bias in a model or dataset?