Answer: Workspace name, Compute name, Access token

The correct parameters for attaching an Azure Databricks compute resource to an Azure Machine Learning workspace are A (Workspace name), B (Compute name), and E (Access token). This is supported by the Microsoft documentation referenced in the community discussion and the high consensus (78%) for ABE. The workspace name identifies the specific Databricks workspace, the compute name is the identifier for the compute resource within Azure ML, and the access token provides authentication. Option C (Workspace user credentials) is incorrect because access tokens are used instead of direct user credentials for security and automation. Option D (Workspace resource ID) is not required; while some community members suggested ADE based on the Python API constructor, the official documentation and majority consensus confirm that the resource ID refers to the compute resource being attached, not the workspace resource ID, making ABE the correct choice.

Author: LeetQuiz Editorial Team