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Answer: MLClient class in Python SDK v2, Azure Machine Learning studio, Azure CLI ml extension v2
The question requires identifying three methods to reconfigure an Azure ML managed compute cluster's node scaling settings. Based on Azure ML documentation and community consensus (BCD with 86% agreement and multiple upvoted comments), the correct methods are: B (MLClient class in Python SDK v2), C (Azure Machine Learning studio), and D (Azure CLI ml extension v2). These three methods allow updating compute cluster properties including minimum and maximum nodes. Option A (Azure Machine Learning designer) is incorrect as it doesn't support compute cluster configuration changes - confirmed by community comments stating 'You cannot scale the instances from the AML Designer.' Option E (BuildContext class in Python SDK v2) is unrelated to compute cluster management and is used for building environments, not compute configuration.
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You have an Azure Machine Learning managed compute resource configured with a minimum of 2 nodes and a maximum of 4 nodes. You need to reconfigure it to have a minimum of 0 nodes and a maximum of 8 nodes.
Which three methods can you use to accomplish this? Each correct answer presents a complete solution.
A
Azure Machine Learning designer
B
MLClient class in Python SDK v2
C
Azure Machine Learning studio
D
Azure CLI ml extension v2
E
BuildContext class in Python SDK v2