Your question is Monitoring Model 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).
What is your strategy for monitoring model drift in a production environment?
Explain how you would detect changes in feature distributions, prediction behavior, and model performance when ground-truth labels may arrive later. Describe alert thresholds, population segments, dashboards, investigation procedures, and retraining or rollback criteria. Include a practical implementation outline that distinguishes data quality issues, covariate drift, concept drift, and training-serving skew.