GCP Professional Data Engineer Practice Question

Your company's data platform team must provide a self-service environment where data engineers across multiple projects can discover, profile, and govern files stored in Cloud Storage and tables in BigQuery. The solution should automatically scan new and existing assets to harvest technical metadata, generate data profiles that include statistics such as null counts and cardinality, and surface the assets through a unified catalog that supports fine-grained access controls. The team wants to minimize custom code and avoid deploying third-party software. Which design best satisfies these requirements?

Create Dataplex lakes and governed zones that reference the Cloud Storage buckets and BigQuery datasets, enable automated discovery, data profiling, and quality scans in each zone, and use the Dataplex catalog for cross-project search and access control.
Use Cloud Asset Inventory to index storage objects and datasets, and schedule BigQuery Data Transfer Service jobs to load audit logs that analysts can query for metadata and quality metrics.
Centralize all data by copying it into a single BigQuery dataset with BigQuery Omni, then rely on INFORMATION_SCHEMA views and custom Cloud Composer DAGs to generate profiling reports.
Register every bucket and dataset in standalone Data Catalog entry groups and trigger Cloud Functions that launch Dataflow jobs to calculate statistics and update metadata tables.

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Answer Description

Dataplex natively unifies governance for Cloud Storage and BigQuery by grouping assets into lakes and zones. When you attach a bucket or dataset to a zone, Dataplex automatically runs discovery jobs that register the assets in the Dataplex (Data Catalog) catalog, creates data profiles with statistics such as null counts, and can run built-in data quality scans. Access to the cataloged assets is controlled through IAM roles on Dataplex resources, giving fine-grained governance without custom pipelines. The other options either rely on separate services that do not provide automated profiling (BigQuery INFORMATION_SCHEMA), require custom functions and pipelines to keep metadata up to date, or use services (Cloud Asset Inventory, BigQuery Data Transfer Service) that are not intended for end-user data discovery and quality management.

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