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Answer: instance_type
The question asks which command parameter in Azure ML Python SDK v2 is used to specify different compute node types when running a command job on an AKS cluster. The correct parameter is 'instance_type', as it directly controls the VM type/size for the compute node. This is supported by the Azure ML documentation and the community discussion where the highest upvoted comments (9 and 6 upvotes) and detailed explanations reference 'instance_type' as the parameter that configures the VM instance type for the job. While 'compute' specifies the target compute resource (AKS1), it does not select different node types within that cluster; 'instance_type' is the parameter that allows selection of specific VM types (e.g., STANDARD_D2_v2) for different compute nodes, aligning with the requirement to target different node types via a command parameter.
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You manage an Azure Machine Learning workspace with a Kubernetes compute target named AKS1, which is an Azure Kubernetes Service (AKS) cluster. The cluster is configured to allow targeting different nodes for training workloads.
You need to run a command job on AKS1 using the Azure ML Python SDK v2. The job must target different types of compute nodes, and the node types must be specified via a command parameter.
Which command parameter should you use to configure the compute node types?
A
environment
B
compute
C
limits
D
instance_type