GCP Professional Data Engineer Practice Question

A global media company stores raw logs in several Cloud Storage buckets across multiple regions and ingests curated data into multiple BigQuery projects that are owned by different business units. Data scientists complain that they cannot easily discover which tables contain video-stream metrics or which buckets store ad-impression logs without asking individual teams. The chief data officer wants a single place where all Cloud Storage objects and BigQuery tables are automatically indexed, enriched with business metadata, and made searchable through a common API while still enforcing existing IAM policies. As the lead data engineer, which design should you implement to satisfy these requirements with minimal custom development and maximum portability across present and future Google Cloud projects?

Use Cloud Asset Inventory to list resources across projects, write Cloud Functions that parse the export into Pub/Sub, and build a Looker dashboard for interactive search.
Enable BigQuery Data Catalog in every project, export catalog entries nightly to a central Cloud SQL instance, and build a custom front-end that merges the exports for search.
Create a Dataplex lake, attach each Cloud Storage bucket and BigQuery dataset as governed assets, define zones and business tags, and rely on the Dataplex Universal Catalog (searchable through Data Catalog APIs) for discovery.
Install an open-source metadata repository such as DataHub on Google Kubernetes Engine, build custom crawlers for Cloud Storage and BigQuery, and expose search through a REST endpoint.

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Answer Description

Dataplex can attach Cloud Storage buckets and BigQuery datasets from any project into logical data lakes and zones. When an asset is attached, Dataplex's built-in metadata service automatically crawls the underlying storage, extracts technical metadata (schemas, locations, partitions), and surfaces it-together with user-defined business tags-in the Dataplex Universal Catalog. The catalog entries reside in Data Catalog, so analysts can use Data Catalog search APIs to look for business terms such as "video-stream metrics" or "ad-impressions" without knowing the physical location of the data. IAM policies applied at the project, dataset, or bucket level are respected because Dataplex manages only references and does not copy data. Running a third-party catalog or exporting metadata to another system would add operational overhead and would not benefit from Dataplex's tight integration with Google Cloud services and IAM, reducing portability and increasing maintenance effort. Therefore, enabling Dataplex, organizing assets into lakes and zones, and relying on the Universal Catalog best meets the stated goals.

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