Preprocess customer data from a restricted GCS bucket for organization-wide analytics while protecting sensitive fields but retaining full data for future use; follow data-privacy requirements.

The correct option uses Dataflow with the DLP API to mask sensitive data before writing it to BigQuery. This approach directly addresses the need to protect sensitive fields while retaining full data for future use, as masking replaces sensitive information with tokens or other non-identifiable data, allowing the original data to be reconstructed if needed. The DLP API is specifically designed for identifying and transforming sensitive data. The first incorrect option, "detect and remove sensitive fields," would lead to data loss, contradicting the requirement to retain full data. The second incorrect option, "Encrypt Cloud Storage with CMEK, query via federated queries, and share key with least privilege," focuses on encryption at rest and federated querying, which doesn't directly address the requirement to preprocess and mask specific sensitive fields within the data itself for organization-wide analytics. The fourth incorrect option, "Use Dataflow with Cloud KMS to encrypt sensitive fields and store encrypted data in BigQuery; share key with least privilege," encrypts the fields but doesn't allow for immediate organization-wide analytics on the masked data without decryption, which could complicate access and increase the risk of exposure.