An organization trains and serves a natural language processing model entirely within Google Vertex AI. The AI governance team wants to include this model in their watsonx.governance inventory with lifecycle tracking and Factsheet documentation. What does watsonx.governance support for this scenario?

watsonx.governance's external model governance capability allows organizations to register AI models built and deployed outside the IBM ecosystem — including Google Vertex AI, Amazon SageMaker, and Microsoft Azure Machine Learning — into the governance platform. Once registered, these external models are associated with governance use cases and their available metadata and performance metrics are captured into AI Factsheets. This enables centralized AI governance across multi-cloud environments, ensuring that all models regardless of where they were built are subject to the same risk assessment, approval, monitoring, and retirement governance processes. The correct answer, "Registration of the external Vertex AI model in watsonx.governance so its metadata and metrics are captured in Factsheets alongside internally built models", directly addresses the scenario described because it aligns with the specific governance requirement in question. The incorrect options ("The NLP model must be re-trained inside IBM Watson Studio before it can be added to the watsonx.governance governance inventory", "The governance team can perform a one-time manual data entry process to import Vertex AI metrics into a Factsheet as a static snapshot", "An IBM middleware agent must be installed inside the Google Cloud project to relay all Vertex AI model metrics to watsonx.governance") may seem plausible but do not satisfy the core requirement. Understanding the distinction between these concepts is critical for IBM watsonx.governance implementations and is frequently tested in the 2.0 AI Lifecycle Governance section of the certification exam.