Your question is Handling Noisy Data in ML. 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).
How do you handle noisy data when building a predictive model?
Explain a practical workflow for identifying measurement errors, outliers, missing values, and potentially incorrect labels without removing useful signal. Discuss preprocessing, model choice, validation, and how you would verify that the model is robust to noise. Include production considerations and Python implementation details where appropriate.