Google Cloud Professional Data Engineer Exam 2025 - Free Practice Questions and Study Guide

Using partitioned tables in BigQuery primarily serves the purpose of enhancing query performance by reducing the amount of data that needs to be scanned during query execution. When a table is partitioned, it is divided into smaller, manageable segments based on a specific column, typically a timestamp or date. This structure allows BigQuery to limit the amount of data read during queries since it only needs to scan the relevant partitions that match the query conditions instead of the entire dataset.

Partitioning is particularly beneficial for large datasets, where scanning all the data can be time-consuming and costly. By querying only the necessary partitions, you can significantly improve response times and reduce resource consumption, which in turn lowers query costs. This efficient data organization method helps streamline data processing and ensure optimal performance in data retrieval tasks.

In contrast, the other choices do not align with the fundamental benefits provided by partitioned tables in BigQuery. Increase in the amount of data scanned during queries is counterproductive, while partitioning does not pertain to data storage options or integration with legacy systems directly.