Answer-first summary for fast verification
Answer: Yes
The solution meets the goal because: 1) Attaching an existing VM (mlvm) as a compute target in Azure ML is supported and allows running training scripts remotely. 2) The Azure ML SDK can log metrics (loss and accuracy) from scripts running on any compute target, including attached VMs. 3) While remote VMs are considered 'unmanaged' and may require extra maintenance, they are fully functional for training and metric logging. The community discussion shows 75% support for 'Yes' (A), with multiple users confirming this approach works in actual exams. The alternative view (B) incorrectly assumes metric logging doesn't work on unmanaged compute, but Azure ML's logging capabilities work across all compute types.
Author: LeetQuiz Editorial Team
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An IT department creates the following Azure resource groups and resources: //IMG//
The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.
Does the solution meet the goal?
A
Yes
B
No