Your question is Post-Deployment Model Monitoring. 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 do you monitor and maintain model performance after deployment in a production environment?
Explain a practical monitoring design covering data quality, feature and prediction drift, delayed ground-truth metrics, alert thresholds, investigation, retraining, rollback, and communication. Include how you would distinguish data drift from concept drift and prevent silent degradation. State the metrics, dashboards, validation checks, and deployment controls you would implement.