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Answer: instance_type
The question asks which command parameter should be used to select different types of compute nodes when running a command job on an AKS cluster using Azure ML Python SDK v2. The correct answer is D (instance_type) because this parameter specifically controls the VM instance type/size for the compute node, allowing selection of different node types within the same AKS compute target. The community discussion shows strong consensus for D (64% of votes), with multiple comments citing official Microsoft documentation that confirms instance_type is used to configure the type of compute instance. While B (compute) specifies the overall compute target (AKS1), it doesn't allow selection between different node types within that target. The instance_type parameter is the correct choice for specifying different VM sizes/node types within the same AKS compute.
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You manage an Azure Machine Learning workspace with a Kubernetes compute target named AKS1, which is an Azure Kubernetes Service cluster configured to target different nodes for training workloads.
You need to run a command job on AKS1 using the Azure ML Python SDK v2. The job must select 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 selection?
A
environment
B
compute
C
limits
D
instance_type