Your question is Handling Missing and Noisy 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 preparing a supervised learning dataset and notice that some fields are missing, inconsistent, or clearly noisy. You want a clean training pipeline that improves model quality without introducing leakage.
How would you handle missing or noisy data in a machine learning dataset?