Your question is Managing Outliers, Noise, and Bias. 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 quality changes a lot across retrains. Some of the instability appears to come from bad records, noisy labels, and uneven performance across groups.
How would you actively identify and manage data issues such as outliers, noise, and biases?