Explanation:
The solution uses Workspace.get() with the specific parameters name='ml-project', subscription_id='sub-id-1', and resource_group='rg-name-1'. This method directly retrieves the workspace using explicit identifiers, which is the correct approach when you know the exact subscription, resource group, and workspace name. It avoids dependency on a config file and ensures the correct workspace is referenced, especially since there are multiple workspaces named 'ml-project' across different subscriptions. The community discussion shows confusion with Workspace.from_config(), but the provided code uses Workspace.get(), which is appropriate here. The answer is 'Yes' because the code will successfully obtain a reference to the specified workspace.
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You have the following Azure subscriptions and Azure Machine Learning service workspaces:
sub-id-1, Workspace Name: ml-projectsub-id-2, Workspace Name: ml-projectYou need to obtain a reference to the ml-project workspace in the sub-id-1 subscription.
Solution: Run the following Python code:
from azureml.core import Workspace
ws = Workspace.get(name='ml-project',
subscription_id='sub-id-1',
resource_group='rg-name-1')
from azureml.core import Workspace
ws = Workspace.get(name='ml-project',
subscription_id='sub-id-1',
resource_group='rg-name-1')
Does the solution meet the goal?
A
Yes
B
No