professional-cloud-data-engineer
Prepare and test your skills
Prepare and test your skills
Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.
Keep the momentum going with these hand-picked practice scenarios
Want more questions like this?
Get a free certification question every week.
Last updated
An enterprise data platform spans multiple Google Cloud projects where distributed teams produce BigQuery analytical datasets and orchestrate machine learning pipelines in Vertex AI. The central governance team needs to implement a unified federated discovery and metadata management solution.
The solution must meet the following requirements:
Which solution should you implement?
Import all distributed LookML project files into a central Looker project and use the LookML metadata panel to trace asset lineage.
Configure Manufacturing Data Engine (MDE) metadata buckets and use the Metadata REST API to register analytical and ML artifacts.
Store all ML and BigQuery pipeline metadata exclusively in project-local Vertex ML Metadata stores and query them using Cloud Composer.
Integrate Vertex ML Metadata with Dataplex Universal Catalog, utilizing Fully Qualified Names (FQNs) for cataloged resources.
Import all distributed LookML project files into a central Looker project and use the LookML metadata panel to trace asset lineage.
Configure Manufacturing Data Engine (MDE) metadata buckets and use the Metadata REST API to register analytical and ML artifacts.
Store all ML and BigQuery pipeline metadata exclusively in project-local Vertex ML Metadata stores and query them using Cloud Composer.
Integrate Vertex ML Metadata with Dataplex Universal Catalog, utilizing Fully Qualified Names (FQNs) for cataloged resources.
Dataplex Universal Catalog acts as a global, cross-project data fabric integrated with Google Cloud services like Vertex AI, BigQuery, and Cloud Composer. Combining it with Vertex ML Metadata creates a centralized catalog that tracks both analytical data assets and machine learning pipeline artifacts.
This approach leverages native Google Cloud governance integrations to achieve global discoverability and automated lineage tracking while respecting distributed domain boundaries.
StudyCerts
StudyCerts