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You manage a BigQuery table storing customer data, which includes sensitive information such as names and addresses. This data must be securely shared with both the data analytics and consumer support teams. The data analytics team should have access to the entire dataset but must be restricted from viewing sensitive information. Conversely, the consumer support team requires access to all data columns but should be limited to customers with active contracts only. You used an authorized dataset and policy tags to enforce these rules. However, the data analytics team has reported that they can still view the sensitive columns despite these precautions. To rectify this, what steps should you take to ensure the data analytics team is restricted from accessing sensitive information? (Choose two.)
A
Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.
B
Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
C
Replace the authorized dataset with an authorized view. Use row-level security and apply filter_expression to limit data access._
D
Remove the bigquery.dataViewer role from the data analytics team on the authorized datasets.
E
Enforce access control in the policy tag taxonomy.