How can you apply a datagroup's caching policy?

The caching policy of a datagroup can be applied to queries on one or a group of Explores, which makes this choice the correct one. Datagroups are utilized in Looker to manage data freshness by controlling when to refresh query results. They provide a mechanism to maintain and optimize performance by enabling caching for specific Explores or connected models.

When the caching policy is defined for a datagroup, it governs how and when the results for queries executed on the designated Explores are updated according to the specified rules. This ensures that users receive the most relevant and timely data without requiring constant re-execution of all queries.

While caching can influence various aspects of Looker, such as dashboards, its primary application lies in defining rules for Explores, assuring that users can efficiently retrieve cached results and improve performance across similar queries. Other options, such as limiting caching to dashboards or report generation, misrepresent the broader utility of datagroups in a LookML framework. Therefore, the correct understanding of the datagroup’s purpose as it relates to Explore queries solidifies why applying its caching policy specifically to those queries is essential.