Microsoft DP-100 Exam With Confidence Using Practice Dumps

You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.

Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.

Box 1: Regenerate the keys of account1.

Azure Blob Storage support authentication through Account key or SAS token.

To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal

Box 2: Update the authentication for store1.

For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks, use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.

Note: Some of the studio's features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.

The following operations are disabled by default in a virtual network:

Preview data in the studio.