Answer-first summary for fast verification
Answer: Use the Azure Machine Learning studio., Run the update method of the AmlCompute class in the Python SDK.
The question asks for three ways to reconfigure an Azure ML compute cluster's node settings. Based on the community discussion and Azure ML documentation: A (Azure ML Studio) is correct as it provides a UI for managing compute resources. B (update method of AmlCompute class) is correct as it directly modifies ScaleSettings programmatically. C (Azure portal) is debated but historically supported; however, recent comments indicate this option was removed, making it unreliable. D (Azure ML designer) is incorrect as it's for pipeline creation, not compute management. E (refresh_state() of BatchCompute) is incorrect as it only refreshes state without reconfiguration. The consensus from highly upvoted comments (e.g., 23 upvotes for ABC initially, but 17 upvotes clarifying C is no longer valid) supports A and B as definitive answers, with C being questionable. Thus, A and B are the optimal choices based on current functionality and best practices.
Author: LeetQuiz Editorial Team
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You create an Azure Machine Learning compute cluster with the following configuration:
You need to reconfigure the cluster to use these values:
What are three possible ways to reconfigure the compute cluster? Each correct answer presents a complete solution.
A
Use the Azure Machine Learning studio.
B
Run the update method of the AmlCompute class in the Python SDK.
C
Use the Azure portal.
D
Use the Azure Machine Learning designer.
E
Run the refresh_state() method of the BatchCompute class in the Python SDK.
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