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GCP Professional Data Engineer Practice Question

Your company operates a healthcare data marketplace built on BigQuery Analytics Hub. You must publish a daily Cloud Storage CSV containing admissions data to an external research partner. Compliance requires that the partner:

  • never receives direct access to the Cloud Storage objects,
  • can query the table only for rows where hospital_region = "EMEA",
  • cannot see the columns patient_ssn and patient_email. Which approach best meets all requirements while minimizing operational overhead?

  • Copy the CSV into a separate project, partition the table by region, delete non-EMEA partitions, and send the partner a signed URL to the sanitized dataset each day.

  • Publish the Cloud Storage bucket as an object listing in Analytics Hub and rely on dynamic data masking policies to hide the PII columns at query time; grant the partner storage.objectViewer on the bucket and analyticshub.subscriber.

  • Create a BigLake external table on the CSV, attach a row-level security policy restricting hospital_region = "EMEA", apply policy tags to mask patient_ssn and patient_email, and publish the table in an Analytics Hub listing; grant the partner the analyticshub.subscriber role only.

  • Load the CSV into a native BigQuery table, create an authorized view that filters the EMEA rows, exclude the PII columns from the view, and share that view in an Analytics Hub listing; grant the partner bigquery.dataViewer on the dataset.

GCP Professional Data Engineer
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