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Answer: Azure Machine Learning compute instance
The question asks what resource should be provisioned first to run a notebook interactively from Azure Machine Learning Studio. A compute instance (Option D) is specifically designed for interactive development work, including running Jupyter notebooks. It provides a pre-configured, managed development environment with the Azure ML SDK and other dependencies installed. While compute clusters (Option C) can run notebooks, they are optimized for training jobs at scale, not interactive development. The default storage account (Option A) is automatically created with the workspace and stores data, but doesn't execute code. Real-time endpoints (Option B) are for deploying trained models for inference, not for development work. The community discussion strongly supports Option D with 100% consensus and upvoted comments explaining that compute instances are purpose-built for interactive notebook execution.
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You create an Azure Machine Learning workspace named workspace1. You create a Python SDK v2 notebook to perform custom model training in workspace1.
You need to run the notebook from Azure Machine Learning Studio in workspace1.
What should you provision first?
A
default storage account
B
real-time endpoint
C
Azure Machine Learning compute cluster
D
Azure Machine Learning compute instance