Private Endpoints for ML Services Quiz

What you'll be asked

1. What is the primary purpose of using a Private Endpoint for an ML service like Azure Machine Learning or AWS SageMaker?
2. When deploying a Private Endpoint for an ML workspace, which DNS configuration is typically required to ensure internal resources can resolve the service's private IP?
3. An ML engineer needs to access a private ML workspace from an on-premises environment. Which infrastructure component is required to facilitate this connectivity alongside the Private Endpoint?
4. If you have configured a Private Endpoint for your ML Workspace, what happens to the public access by default if 'Public Network Access' is disabled in the workspace settings?
5. You are troubleshooting a connection issue where a compute instance inside a VNet cannot reach a storage account protected by a Private Endpoint. What is the most likely cause?

Answers and explanations come after you submit.