Which step must also be completed to put the proposed query into production?

Putting a proposed query into production often requires ensuring that the query is optimized for ongoing operational use. Specifying a new checkpoint location is crucial when working with Delta tables or streaming data in Databricks to ensure that the query can resume from a specific point in case of failures or maintenance. This guarantees data consistency and checkpoint management for reliability in production environments.

In production scenarios, it's essential to manage state effectively; hence defining a new checkpointLocation aids in tracking the progress of data processing. This also helps with query performance as well as recovery from failures, making it a critical step for deploying queries.

The other actions, such as increasing the shuffle partitions or refreshing tables, play a role in optimizing performance or managing specific datasets but do not directly address the resilience and management of the data flow in a production context. Likewise, registering data to the Hive metastore is related to metadata management rather than the operational stability of a running query.