Securing an Azure Machine Learning Network Before training models, it’s crucial to secure your Azure Machine Learning network against potential threats. If left unprotected, sensitive data and models could be vulnerable to malicious attacks , leading to data breaches or model tampering. Key Security Measures: Connect to the workspace via a private endpoint (private IP) to ensure controlled access. The private endpoint can be added using: Azure Machine Learning Python SDK Azure CLI Networking tab in the Azure portal Restrict workspace access to occur only via private IP addresses . Other Azure services interacting with the workspace must also be behind the VNet . Any development environment using the workspace must belong to the VNet , unless configured to allow public IP connections . Question: Which of the following methods can be used to connect to the secure workspace ? (Select three.)