When configuring Watson OpenScale to monitor models, an administrator must connect it to the deployment environment where the models are hosted. How is this connection established?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think of it this way: in real-world AI governance, add the watson machine learning deployment space as a machine learning provider within watson openscale configuration is exactly what teams reach for when they need to handle this scenario. Watson OpenScale requires each deployment environment to be registered as a machine learning provider within OpenScale's configuration. On the exam, remember that this falls squarely under the 3.0 Configure watsonx.governance domain.
Full explanation below image
Full Explanation
Watson OpenScale requires each deployment environment to be registered as a machine learning provider within OpenScale's configuration. This registration includes providing the service URL and API credentials so that OpenScale can authenticate to the deployment space, enumerate deployed models, and collect scoring payload data. The correct answer, "Add the Watson Machine Learning deployment space as a machine learning provider within Watson OpenScale configuration", directly addresses the scenario described because it aligns with the specific governance requirement in question. The incorrect options ("Import deployment space credential JSON files into AI Factsheet custom metadata fields for each governed asset", "Enable the deployment space sync toggle in OpenPages General Settings to authorize OpenScale access", "Grant the OpenScale service ID access via IBM Cloud IAM policies only; no additional configuration is needed inside Watson OpenScale itself") 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 3.0 Configure watsonx.governance section of the certification exam.