A local workspace configuration file and invoking Workspace.from_config in the Python SDK is the correct option.

A local workspace configuration file stores the workspace identification and connection details that the SDK needs and Workspace.from_config is the Python SDK helper that reads that file and returns a configured Workspace object you can use from your local environment. This approach avoids hard coding credentials and lets the SDK locate the workspace by name and project settings so your local code can interact with the cloud workspace.

A Compute Engine virtual machine with CPU or GPU resources is incorrect because you do not need to run on a cloud VM to connect the Python SDK to a workspace. A VM may provide compute capacity but it is not required for establishing the SDK connection.

A Google Cloud service account with application default credentials is incorrect in this context because the question specifies using the workspace configuration and the SDK helper. Application default credentials are used by some Google Cloud client libraries but they are not the required mechanism for Workspace.from_config.

An App Engine deployment with environment variables set for the workspace is incorrect because deploying to App Engine is not necessary to connect from a local Python environment. Environment variables can work for configuration in some workflows but the standard local method is the configuration file plus Workspace.from_config.

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