Welcome to your interview.
The question is on your right: Data Quality in ML Pipelines. Take a moment with it first.
Talk your thinking through with me if you like - when you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes). Discussion and graded submissions share your five interviewer interactions, so spend them well.
You're building and maintaining data pipelines that feed model training and batch scoring. Before focusing on model changes, you want a clear approach for keeping the underlying data trustworthy as it moves through ingestion, transformation, and feature creation.
How do you ensure the quality of data used in your models?