A company currently serves its ML model on 10 Reserved accelerated instances and must deploy a new model version to an Amazon SageMaker real-time endpoint. The deployment must continue to use the original 10 instances to serve both versions, include one extra Reserved Instance to use during deployment, and perform the transition with no downtime. Which deployment approach satisfies these constraints?

A rolling deployment with a rolling batch size of 1 is the correct approach. This strategy updates instances one by one, ensuring continuous availability and allowing the new model version to be deployed without downtime. The extra Reserved Instance can be used as the first instance to be updated, maintaining the original capacity throughout the process. Blue/green deployments typically involve creating a completely new environment (green) alongside the existing one (blue) and then shifting traffic. While this offers no downtime, it would require 10 additional instances, not just one, to match the original capacity during the transition. All-at-once or canary traffic shifting within a blue/green setup still requires the full new environment. A shadow test is for evaluating model performance without impacting live traffic and doesn't directly address the deployment of a new version to serve requests.