You use an Azure Machine Learning workspace.You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.What should you do?
Answer(s): A
To control token authentication, use the token_auth_enabled parameter when you create or update a deploymentToken authentication is disabled by default when you deploy to Azure Kubernetes Service.Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods: key-based: A static key is used to authenticate to the web service. token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.Incorrect Answers:C: Token authentication isn't supported when you deploy to Azure Container Instances.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service
HOTSPOT (Drag and Drop is not supported)You are the owner of an Azure Machine Learning workspace.You must prevent the creation or deletion of compute resources by using a custom role. You must allow all other operations inside the workspace.You need to configure the custom role.How should you complete the configuration? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: Microsoft.MachineLearningServices/workspaces/*/readReader role: Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers can't create or update these assets.Box 2: Microsoft.MachineLearningServices/workspaces/*/writeIf the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.Box 3: Box 2: Microsoft.MachineLearningServices/workspaces/computes/*/deleteBox 4: Microsoft.MachineLearningServices/workspaces/computes/*/write
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-user-has-access-to-a-resource
HOTSPOT (Drag and Drop is not supported)You create an Azure Machine Learning workspace named workspace1. You assign a custom role to a user of workspace1.The custom role has the following JSON definition:Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: No The actions listed in NotActions are prohibited.If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.Box 2: No Deleting compute resources in the workspace is in the NotActions list.Box 3: Yes Writing metrics is not listed in NotActions.
HOTSPOT (Drag and Drop is not supported)You create a new Azure Databricks workspace.You configure a new cluster for long-running tasks with mixed loads on the compute cluster as shown in the image below.Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: No Running user code in separate processes is not possible in Scala.Box 2: No Autoscaling is enabled. Minimum 2 workers, Maximum 8 workers.
https://docs.databricks.com/clusters/configure.html
HOTSPOT (Drag and Drop is not supported)You use an Azure Machine Learning workspace. The default datastore contains comma-separated values (CSV) files.The CSV files must be made available for use in experiments and data processing pipelines. The files must be loaded directly into pandas dataframes.How should you complete the code? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.
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