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Answer: Initialize and specify the ResourceConfiguration class.
The question requires configuring serverless compute with specific instance type and count using Azure ML Python SDK v2. Option C is correct because the ResourceConfiguration class is specifically designed for this purpose in serverless compute scenarios. The community discussion shows strong consensus (75% support for C) with multiple comments confirming that ResourceConfiguration is the proper approach. Option A is incorrect as it refers to specifying compute name, not instance configuration. Option B is invalid as 'tier parameter to Dedicated VM' doesn't apply to serverless compute. Option D is incorrect because AmlCompute class is used for managed compute clusters, not serverless compute. The Microsoft documentation referenced in the discussion confirms ResourceConfiguration is the correct class for configuring instance type and count in serverless compute jobs.
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You are managing an Azure Machine Learning workspace and designing a training job configured to use a serverless compute. The serverless compute requires a specific instance type and count.
How should you configure the serverless compute using the Azure Machine Learning Python SDK v2?
A
Specify the compute name by using the compute parameter of the command job.
B
Configure the tier parameter to Dedicated VM.
C
Initialize and specify the ResourceConfiguration class.
D
Initialize AmiCompute class with size and type specification.