Microsoft AZ900 Exam Dumps|QAs Page -15

Last updated on July 14th, 2024 at 12:16 am

Microsoft AZ900 Exam Dumps post contains real and latest questions for Microsoft Azure Fundamentals.

Microsoft AZ900 Exam Dumps – QAs 71-75

Q71. You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.
You create the following config.json file.
{
“workspace_name” : “ml-workspace”
}
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

1. login
2. resource_group
3. subscription_id
4. key
5. region

Q72. You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
✑ Minimum nodes: 2
✑ Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
✑ Minimum nodes: 0
✑ Maximum nodes: 8
You need to reconfigure the compute resource.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

1. Use the Azure Machine Learning studio.
2. Run the update method of the AmlCompute class in the Python SDK.
3. Use the Azure portal.
4. Use the Azure Machine Learning designer.
5. Run the refresh_state() method of the BatchCompute class in the Python SDK.

Q73. 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.

1. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, and resource_group parameters.
2. Navigate to Azure Machine Learning studio and create a workspace.
3. 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 ג€”w and ג€”g parameters for the workspace name and resource group.
4. Navigate to Azure Machine Learning studio and create a workspace.
5. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.

Q74. HOTSPOT –
You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.
You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.
You need to configure store1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

Q75. DRAG DROP –
You are using a Git repository to track work in an Azure Machine Learning workspace.
You need to authenticate a Git account by using SSH.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

Correct Answer

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration