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GCP Professional Cloud Security Engineer Practice Question

A multinational retailer stores raw event logs in several Google Cloud projects. Some logs reside in regional Cloud Storage buckets, while others are streamed into BigQuery datasets by a Dataflow pipeline. The security team must continuously discover any newly ingested credit-card or government-ID data, calculate risk scores, and view results in one central Sensitive Data Protection dashboard. They want to avoid scheduling a separate inspection job for every individual bucket or dataset, but are willing to perform a one-time setup in each project. Which approach best meets these requirements?

  • Attach Data Catalog policy tags to BigQuery tables and rely on policy insights to detect new credit-card or government-ID data automatically.

  • Enable organization-level Sensitive Data Protection discovery for Cloud Storage, and in every project configure a BigQuery data profile scan so all datasets are automatically profiled and results appear in the Sensitive Data Protection dashboard.

  • Create individual Cloud DLP inspection jobs for each Cloud Storage bucket and BigQuery dataset, then aggregate the findings in Cloud Logging.

  • Use Cloud Asset Inventory to export metadata for all storage resources and query it in BigQuery to locate sensitive fields.

GCP Professional Cloud Security Engineer
Ensuring data protection
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