PMI Cognitive Project Management for AI (CPMAI) Practice Test

In production AI, keeping systems reliable requires continuous monitoring, retraining, and governance. Monitoring watches how the model performs over time, checks data quality, and detects drift or anomalies so issues are spotted early. When drift or performance changes occur, retraining updates the model with fresh data to restore accuracy and align with current conditions. Governance provides the oversight framework—policies, approvals, audit trails, and model registry—to manage changes, ensure compliance, and maintain accountability. Without these ongoing practices, a one-time deployment can degrade, ignoring drift leads to poor results, and no governance removes essential control and visibility over the system.