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Free DP-100 Exam Braindumps (page: 27)

Page 26 of 127
PDF with 562 Questions

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?

  1. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true
  2. Deploy the model to Azure Container Instances. During deployment, set the auth_enabled parameter of the target configuration object to true
  3. Deploy the model to Azure Container Instances. During deployment, set the token_auth_enabled parameter of the target configuration object to true
  4. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.enabled parameter of the target configuration object to true

Answer(s): A

Explanation:

To control token authentication, use the token_auth_enabled parameter when you create or update a deployment
Token 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.


Reference:

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:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: Microsoft.MachineLearningServices/workspaces/*/read
Reader 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/*/write
If 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/*/delete
Box 4: Microsoft.MachineLearningServices/workspaces/computes/*/write


Reference:

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:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


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.


Reference:

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 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:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


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.


Reference:

https://docs.databricks.com/clusters/configure.html






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