Professional-Data-Engineer Exam Dumps | You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket,

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Google.Professional-Data-Engineer.v2025-04-23.q181
Question 174

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Question 174/181

You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries What should you do?

A. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

B. Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

C. Change the storage class of the Hive partitioned data objects from Coldline to Standard.

D. Upgrade the external table to a BigLake table Enable metadata caching for the table.

Correct Answer: D

BigLake is a Google Cloud service that allows you to query structured data in external data stores such as Cloud Storage, Amazon S3, and Azure Blob Storage with access delegation and governance. BigLake tables extend the capabilities of BigQuery to data lakes and enable a flexible, open lakehouse architecture. By upgrading an external table to a BigLake table, you can improve the performance of your queries by leveraging the BigQuery storage API, which supports data format conversion, predicate pushdown, column projection, and metadata caching. Metadata caching reduces the number of requests to the external data store and speeds up query execution. To upgrade an external table to a BigLake table, you can use the ALTER TABLE statement with the SET OPTIONS clause and specify the enable_metadata_caching option as true. For example:
SQL
ALTER TABLE hive_partitioned_data
SET OPTIONS (
enable_metadata_caching=true
);
AI-generated code. Review and use carefully. More info on FAQ.
Reference:
Introduction to BigLake tables
Upgrade an external table to BigLake
BigQuery storage API

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