Many students tell me Domain V feels abstract. The tasks read like a list: oversee model governance, oversee AI solution metrics, prepare the final report. On the exam they turn into very concrete situations, usually set weeks or months after deployment. Here is a pattern I see a lot. A retailer deploys a demand forecasting model. Accuracy at go-live is fine. Three months later, store managers stop trusting the forecasts, because a competitor opened nearby and buying patterns changed. Nobody noticed for weeks, because the dashboard only showed system uptime. Three questions usually decide the answer in these scenarios. 1. Who owns the model now? After the transition, someone in operations must own performance, drift checks and the retraining decision. If the stem tells you nobody owns it, fixing ownership is often the first step. 2. What is being measured? Uptime and response time are not enough. You need the model metric that was agreed (forecast error, recall, and so on) and the business KPI the model was meant to move, each with a threshold and an alert. 3. What happens when a threshold is breached? A good governance plan says in advance who gets the alert, who decides between retraining, rollback or a fallback process, and how the change is versioned and approved. A common trap is the option that says "retrain the model immediately". Retraining is a decision, not a reflex. First confirm what changed: the input data, the relationship between inputs and outcome, or a broken pipeline. Then follow the change control the team agreed. Exam link: Domain V, Task 3 (Oversee model governance) and Task 4 (Oversee AI solution metrics). Question for you: in your own projects, who owns a model after it goes live? Is it written down anywhere?