Explanation:
The correct answer is B because Intellisense in Azure Machine Learning Studio notebooks requires a running compute instance to provide code completion and inline error highlighting. The community discussion shows strong consensus for option B with 100% of answers selecting B and multiple comments explaining that Intellisense needs a running compute instance. While some comments mention using pip magic functions (options C and D) for package installation, these are not directly related to enabling Intellisense itself - they may be needed for specific packages but don't enable the core Intellisense functionality. Option A (stopping the compute instance) would actually disable Intellisense by shutting down the compute resources needed for code analysis.
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You create an Azure Machine Learning workspace that includes a compute instance using Azure Machine Learning Studio. You are developing a Python SDK v2 notebook within this workspace.
To enable IntelliSense in the notebook, what should you do?
A
Stop the compute instance.
B
Start the compute instance.
C
Run a %pip magic function on the compute instance.
D
Run a !pip magic function on the compute instance.
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