Your company has a Google Cloud project that uses BigQuery for data warehousing. There are some tables that contain personally identifiable information (PII). Only the compliance team may access the PII. The other information in the tables must be available to the data science team. You want to minimize cost and the time it takes to assign appropriate access to the tables. What should you do?

The correct solution involves creating a separate dataset for the data science team and using authorized views. Creating a new dataset isolates the views from the original PII-containing tables. Views allow you to select specific columns, effectively excluding PII, while still querying the underlying data. Authorizing the view to access the source dataset ensures the view can read the necessary data without granting the data science team direct access to the original PII tables. Assigning project-level IAM roles to the data science team and then dataset-level access controls to the new dataset containing the views provides granular control. Materialized views are not ideal here because they store a copy of the data, which could incur additional storage costs and requires refreshing, whereas standard views query the data directly. The other incorrect options either do not create a separate dataset for the views, which is crucial for separation of concerns, or suggest using materialized views, which is less efficient for this use case.