Your question is Monitor and Retrain for Data Drift. 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).
How would you architect a pipeline to monitor and retrain models based on data drift in production?
Explain how you would collect production features and predictions, calculate drift against a reference dataset, trigger and orchestrate retraining, validate candidate models, and deploy them safely. Include data quality checks, lineage, model versioning, rollback, monitoring, alerting, and strategies for backfills and failed jobs.
