GCP Professional Data Engineer Practice Question

Your company, a global retailer subject to GDPR, stores transactional data in a BigQuery table called customer_orders that has the columns order_id, item_id, customer_email, credit_card_hash, and amount. Marketing analysts must be able to run ad-hoc SQL on every column except customer_email and credit_card_hash, while the Risk team needs unrestricted access. The solution must scale so that any new columns later classified as PII are automatically protected without rewriting queries or creating additional tables. How should you implement this in BigQuery?

Move customer_email and credit_card_hash into a separate BigQuery table, restrict access to that table to the Risk team, and let Marketing query the remaining columns in the original table.
Build an authorized view that omits the customer_email and credit_card_hash columns, share the view with Marketing analysts, and share the underlying table directly with the Risk team.
Encrypt only the customer_email and credit_card_hash columns with customer-managed encryption keys (CMEK) and provide the decryption key to the Risk team but not to Marketing analysts.
Create a Data Catalog taxonomy with a PII policy tag, attach the tag to customer_email and credit_card_hash, grant the Risk group permissions to read that policy tag and the dataset, and give Marketing only dataset-level BigQuery read access without tag permission.

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Answer Description

BigQuery enforces column-level security through policy tags that live in Data Catalog taxonomies. By tagging each sensitive column with a PII policy tag and granting access to that tag only to the Risk group (via the Data Catalog Fine-Grained Reader or BigQuery Data Policy User role), you ensure they can query the protected columns while Marketing-who has dataset-level read rights but no access to the tag-cannot. When new PII columns are added, attaching the same policy tag immediately protects them, so no views or table restructuring is required. Row-level security filters rows, not columns; splitting tables or using authorized views would work but requires ongoing schema maintenance; per-column encryption with customer-managed keys is not natively enforced by BigQuery for column masking and would block all users without the key, not only Marketing.

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