Your question is Preventing Overfitting on Small 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're training a supervised model with limited data, and the labels or features may be noisy. You want the model to generalize well instead of memorizing artifacts in the sample.
What techniques do you use to prevent overfitting when working with small or highly noisy datasets?