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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 (ResourceConfiguration class) is correct because it's specifically designed for setting compute resources like instance_count and instance_type in serverless compute scenarios. The community discussion shows 75% consensus for C, with multiple comments confirming ResourceConfiguration is the proper approach for serverless compute configuration. Option A is incorrect as it refers to specifying compute name, not resource configuration. Option B is invalid as 'tier parameter to Dedicated VM' doesn't apply to serverless compute. Option D (AmlCompute class) is incorrect because AmlCompute is used for managed compute clusters, not serverless compute.
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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 instance count.
Using the Azure Machine Learning Python SDK v2, how do you configure the serverless compute?
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.
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