Diagnose an Incident: Code Deploy or Model Promotion?

At 14:00, your team ships a serving-code release (a refactor of the feature-fetching client) to production. Separately, at 14:10, a model that finished its staging validation earlier that day gets promoted to "production" in the model registry — a routine, independently-scheduled promotion unrelated to the 14:00 code deploy. At 14:20, the on-call is paged: prediction latency is normal, but the positive-prediction rate has jumped sharply.

1. Why is it important that the code deploy and the model promotion are independently tracked events, given this incident?
2. Describe the diagnostic steps you'd take to determine whether the code deploy or the model promotion (or something else) caused the jump.
3. Suppose you determine the model promotion is the cause. What are the mechanics of the fix, and why is it a materially faster and safer operation than it would be if the model were baked into the serving code's container image?