Your team maintains 1PB of sensitive data within BigOuery that contains personally identifiable information (PII). You need to provide access to this dataset to another team within your organization…

Question

Options

• AUtilize BigQuery's row-level access policies to mask PII columns based on the other team's user
• BExport the BigQuery dataset to Cloud Storage. Create a VPC Service Control perimeter and allow
• CImplement data pseudonymization techniques to replace the PII fields with non-identifiable
• DCreate a filtered copy of the dataset and replace the sensitive data with hash values in a separate

• A
  72% (23)
• B
  3% (1)
• C
  19% (6)
• D
  6% (2)

Explanation

BigQuery provides native, fine-grained data access controls including column-level security via policy tags and data masking rules. By applying row-level access policies and column masking policies tied to the other team's identity, PII columns can be masked or tokenized in-place - the other team sees a redacted view of the same dataset without any data duplication or ETL. Options C and D both create separate copies of the data (pseudonymized or hashed), which introduces data freshness issues, storage costs, and synchronization complexity. Option B exports data to Cloud Storage and adds a VPC Service Controls perimeter, which is architecturally heavy and still doesn't mask PII within the export. The native BigQuery solution in Option A minimizes management overhead while protecting PII directly at the data layer.

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