Professional Data EngineerEnsuring solution qualityHard
A multinational retail company is building a new customer analytics platform on Google Cloud. They store customer personal identifiable information (PII) in BigQuery and need to comply with GDPR and CCPA regulations. This requires ensuring that customer data can be securely deleted upon request (right to be forgotten) and that data access is restricted based on user roles and data sensitivity. Which BigQuery features should they combine to meet the 'right to be forgotten' and granular access control requirements?
- ABigQuery row-level security and data deletion with DML statements
- BCloud DLP for PII redaction and BigQuery authorized views
- CBigQuery column-level security and data expiration policies
- DCloud Audit Logs and BigQuery table snapshots
Show answer & explanationAnswer & explanation
Correct answer: A. BigQuery row-level security and data deletion with DML statements
BigQuery row-level security allows fine-grained access control to specific rows (e.g., a customer's data) based on user identity or roles, satisfying granular access control. Data deletion using DML (DELETE) statements in BigQuery, combined with row-level security to identify the specific data, directly addresses the 'right to be forgotten' requirement by permanently removing customer data from the table.
Why the other options are wrong
- B. Cloud DLP redacts data, but doesn't handle the 'right to be forgotten' (deletion) or granular access control. Authorized views provide access to query results, not direct row-level control.
- C. Column-level security restricts access to specific columns, not rows. Data expiration policies delete data after a set time, not on explicit request.
- D. Cloud Audit Logs track data access, and table snapshots create backups; neither addresses granular access control or the 'right to be forgotten' deletion requirement.
BigQuery Right to Be Forgotten & Row-Level Security
The ability to delete specific customer data upon request using DML statements, combined with granular access control to individual data rows via BigQuery row-level security.
- DML DELETE statements permanently remove data.
- Row-level security policies filter rows visible to users.
- Essential for GDPR, CCPA, and other data privacy regulations.
Memory trick: Rows Remove Rights, Roles Restrict.