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NEW QUESTION: 1
You create a new Azure subscription. No resources are provisioned in the subscription.
You need to create an Azure Machine Learning workspace.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying -w and -g parameters for the workspace name and resource group.
B. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
C. Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/ workspaces resource and its dependencies.
D. Navigate to Azure Machine Learning studio and create a workspace.
E. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.
Answer: A,C,D
Explanation:
Explanation
B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning.
Example:
{"type": "Microsoft.MachineLearningServices/workspaces",
...
C: You can create a workspace for Azure Machine Learning with Azure CLI Install the machine learning extension.
Create a resource group: az group create --name <resource-group-name> --location <location> To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w <workspace-name> -g <resource-group-name> D: You can create and manage Azure Machine Learning workspaces in the Azure portal.
* Sign in to the Azure portal by using the credentials for your Azure subscription.
* In the upper-left corner of Azure portal, select + Create a resource.
* Use the search bar to find Machine Learning.
* Select Machine Learning.
* In the Machine Learning pane, select Create to begin.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace

NEW QUESTION: 2
HOTSPOT


Answer:
Explanation:

NEW QUESTION: 3
Which three statements are true about Data Guard configurations? (Choose three.)
A. All databases in one Data Guard environment must have the same database name.
B. The Oracle recommendation for the number of standby redo log groups per thread is one more than the number of online redo log groups per thread.
C. For Standard Edition, LOG_ARCHIVE_DUPLEX_DEST is used to configure redo transport, from the primary to the standby database.
D. VALID_FOR is a LOG_ARCHIVE_DEST_N attribute that enables DB role change operations without having to modify LOG_ARCHIVE_DEST_n when performing switchovers or failovers.
E. Up to 30 physical standby databases may exist within one configuration.
F. When using the Data Guard Broker, an spfile is not required.
Answer: A,B,E
Explanation:
Explanation/Reference:
Explanation:
A: Parameter DB_NAME
On a primary database, specify the name used when the database was created. On a physical standby database, use the DB_NAME of the primary database.
E: A Data Guard configuration includes a production database referred to as the primary database, and up to 30 directly connected replicas referred to as standby databases.
F: The Far Sync instance's standby redo logs (SRLs) should have the same number of redo log groups as on the primary +1 for each thread as per standard MAA Best Practices.
Incorrect:
not D: You must use a server parameter file (SPFILE) to ensure the broker can persistently reconcile values between broker properties and any related initialization parameter values.
References:
https://docs.oracle.com/database/121/SBYDB/create_ps.htm#SBYDB4720
https://docs.oracle.com/database/121/DGBKR/install.htm#DGBKR090