Your question is Handling Missing Noisy Biased 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 working on a supervised learning problem and find that parts of the dataset are incomplete, some labels or features look noisy, and the sample may not represent the population you care about.
How would you handle missing, noisy, or biased data in your research?