GCP Professional Cloud Security Engineer Practice Question
Your security team must automatically locate credit card numbers embedded in several years of transaction logs stored in both Cloud Storage buckets and BigQuery datasets. After discovery, they need a scheduled process that generates a compliance report and writes a de-identified version of the data, with card numbers hashed, into a separate analytics project. They want to minimize custom code and operational overhead. Which Google Cloud capability best satisfies these requirements?
Custom data processing pipelines on Confidential VMs using Cloud Dataflow
CMEK-encrypted buckets and datasets managed through Cloud KMS
BigQuery row-level security policies combined with Data Catalog policy tags
Sensitive Data Protection inspection jobs with de-identification templates
Sensitive Data Protection (formerly Cloud DLP) is designed to scan data at rest in Cloud Storage, BigQuery and other Google Cloud repositories using inspection jobs that can be scheduled or triggered. It offers built-in infoType detectors for credit card numbers, produces detailed findings reports, and can apply de-identification transformations such as hashing with a reusable template. The other options address access control, encryption at rest, or confidential execution, but none automate discovery and de-identification of sensitive data across storage services.
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GCP Professional Cloud Security Engineer
Ensuring data protection
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