A city transportation department is deploying an AI model that adjusts traffic signal timing.

PMI-CPMAI emphasizes that AI solutions require lifecycle governance, including operational controls that sustain trustworthy performance in changing real-world conditions. The PMI-CPMAI exam outline highlights practices such as maintaining audit trails and applying responsible and trustworthy AI oversight as part of operationalization. In dynamic environments like traffic control, model drift and data drift are expected: shifts in commuting behavior, roadworks, special events, and weather can change the distributions the model sees. The most PMI-aligned method is continuous monitoring and auditing, which supports early detection of performance degradation, emerging bias, and safety-impacting behaviors, and enables controlled remediation (retraining, threshold adjustments, rollback plans). Simply increasing training data once (B) does not address ongoing change. Disabling updates (C) can lock in outdated behavior and increase harm over time. Vendor guarantees (D) do not replace the organization’s accountability obligations under trustworthy AI principles (ethics, responsibility, governance, transparency).