Explanation:
The solution does NOT meet the goal because the Workspace.get() method requires the correct subscription_id and resource_group parameters to uniquely identify the workspace. In the given scenario, the ml-project workspace is in Subscription 1, but the code uses 'sub1' as subscription_id and 'rg1' as resource_group without confirming these are the actual values. The community discussion strongly supports this with 87% choosing 'No' and multiple comments explaining that Workspace.get() requires the actual subscription ID and resource group name, not placeholder values. The most upvoted comments (6 and 4 upvotes) clearly state that without the correct subscription_id and resource_group parameters, the method cannot locate the workspace. While some comments suggested subscription_id and resource_group are optional, the consensus and higher-voted responses indicate they are required for unambiguous workspace identification, especially when multiple subscriptions and workspaces exist.
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You have the following Azure subscriptions and Azure Machine Learning service workspaces:
ml-prod in the eastus region.ml-project in the westus region.ml-dev in the westus region.You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:
from azureml.core import Workspace
ws = Workspace.get(name='ml-project',
subscription_id='sub1',
resource_group='rg1')
from azureml.core import Workspace
ws = Workspace.get(name='ml-project',
subscription_id='sub1',
resource_group='rg1')
Does the solution meet the goal?
A
Yes
B
No