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 machine learning pipelines, and the quality of training and inference data directly affects model behavior. You want a clear approach for catching bad data early, keeping runs reproducible, and making pipeline failures visible.
How do you ensure data quality in your machine learning projects?