Your question is Detecting Data Drift Over Time. 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 detect and mitigate data drift in deployed machine learning models over extended periods?
Explain a practical monitoring design covering feature distributions, missingness, prediction distributions, and delayed ground-truth performance. Include statistical tests, alert thresholds, investigation steps, retraining or rollback policies, and safeguards against false alarms and data-quality incidents.