DP-100 Question #253: Real Exam Question with Answer & Explanation

Question

You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count. You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?

Options

• ASpecify the compute name by using the compute parameter of the command job.
• BConfigure the tier parameter to Dedicated VM.
• CInitialize and specify the ResourceConfiguration class.
• DInitialize AmiCompute class with size and type specification.

Explanation

To configure a serverless compute with a specific instance type and count for an Azure Machine Learning training job using SDK v2, you must utilize the ResourceConfiguration class.

Common mistakes.

• A. Specifying a compute name using the compute parameter points to an existing, pre-provisioned compute target, not a serverless compute configured on the fly with specific instance details.
• B. The tier parameter is not the correct mechanism within the SDK v2 to define serverless compute instance types or counts for a job.
• D. AmiCompute is not a standard class in Azure Machine Learning Python SDK v2 for configuring serverless compute resources; it seems to refer to AWS EC2 AMIs.

Concept tested. Azure ML serverless compute configuration

Reference. https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.entities.resourceconfiguration?view=azure-ml-py

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