DP-100 · Question #290
You have an Azure Machine Learning (ML) model deployed to an online endpoint. You need to review container logs from the endpoint by using Azure ML Python SDK v2. The logs must include the console log
The correct answer is B. Create an instance of the MLCIient class.. When using Azure ML Python SDK v2, the entry point for all operations is the MLClient class. You must first instantiate MLClient (option B) with your subscription ID, resource group, and workspace name to establish a connection to the workspace. Only after creating this client in
Question
Options
- AConnect by using SSH to the inference server.
- BCreate an instance of the MLCIient class.
- CConnect by using Docker tools to the inference server.
- DCreate an instance of the OnlineDeploymentOperations class.
How the community answered
(56 responses)- A4% (2)
- B86% (48)
- C2% (1)
- D9% (5)
Explanation
When using Azure ML Python SDK v2, the entry point for all operations is the MLClient class. You must first instantiate MLClient (option B) with your subscription ID, resource group, and workspace name to establish a connection to the workspace. Only after creating this client instance can you call methods like online_deployments.get_logs() to retrieve container logs from the inference server. SSH (A) and Docker tools (C) are direct server access methods, not SDK approaches. OnlineDeploymentOperations (D) is not directly instantiated-it is accessed through the MLClient instance.
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