You are designing an Azure Machine Learning solution for traffic optimization. The model must be deployed as a web service on a serverless compute and provide real-time predictions based on current…

Question

Options

• AAzure Machine Learning online endpoint
• BAzure Machine Learning batch endpoint
• CAzure Machine Learning serverless compute
• DAzure Machine Learning Kubernetes online endpoints

• A
  79% (33)
• B
  2% (1)
• C
  7% (3)
• D
  12% (5)

Explanation

Azure Machine Learning managed online endpoints are the correct choice for real-time, web-service-style inference on serverless compute. They automatically provision and manage the underlying infrastructure, support synchronous (real-time) predictions, and scale without requiring the user to manage VMs. Batch endpoints (choice B) are for asynchronous large-scale scoring, not real-time. 'Azure Machine Learning serverless compute' (choice C) refers to training compute, not inference hosting. Kubernetes online endpoints (choice D) require a self-managed Kubernetes cluster, which contradicts the serverless requirement.

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