What should you do to access a source table in a different project while using Vertex AI?

Accessing a source table in a different project while using Vertex AI requires proper authorization and permissions to ensure that the service can interact with datasets securely and according to best practices. Providing the BigQuery Data Editor role to the Vertex AI service account in the project that contains the source table grants the necessary permissions to read from the table and perform operations as needed.

This approach adheres to the principle of least privilege, allowing the service account to access only the resources necessary for its function without requiring copies of datasets, which can lead to data consistency issues. It also maintains a clear separation of resources across projects while enabling collaborative access to data when appropriately configured.

In contrast, simply copying the table to your project would create redundancy and potential synchronization issues, while changing project settings can complicate configurations and may not necessarily resolve permission issues. Using the default service account might not have the required privileges to access resources in other projects, particularly if it has not been granted the appropriate roles or if access is restricted based on IAM policies.