Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!
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.
An e-commerce company is evolving its data strategy from simple raw log archiving in Cloud Storage to an active operational intelligence and machine learning ecosystem. Multiple data science and analytics teams need to spin up ephemeral, short-lived compute clusters running Apache Spark and Hive to process massive datasets and drive personalized customer recommendations.
The architecture must preserve table schemas, column statistics, and partition definitions across transient cluster lifecycles while enabling seamless metadata interoperability with Google Cloud services like BigQuery and Dataplex Universal Catalog.
Which solution should you implement to manage the centralized data lake metadata?
Dataproc Metastore is a fully managed, serverless, highly available Apache Hive metastore (HMS) that runs on Google Cloud. It acts as a central technical metadata repository, tracking schemas, partitions, data types, and column statistics for datasets residing in a cloud data lake.
Dataproc Metastore directly supports the architectural progression from basic archival storage to real-time analytics and advanced ML by decoupling metadata from ephemeral processing resources, ensuring seamless multi-engine interoperability.
Keep the momentum going with these hand-picked practice scenarios
Want more questions like this?
Get a free certification question every week.
StudyCerts
StudyCerts