Your question is Handling 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're preparing a supervised learning dataset and notice suspicious extreme values, inconsistent labels, and uneven representation across groups. You want a training set that supports good generalization without hiding real signal.
How would you manage data issues such as outliers, noise, and biases in training datasets?