Which statement about stream-static joins and static Delta tables is accurate?

The statement indicating that each microbatch uses the most recent static Delta table version each time is accurate because it reflects how stream-static joins operate in Databricks. In a streaming scenario, when a static Delta table is joined with a streaming DataFrame, each microbatch can access the latest committed version of that static Delta table, ensuring that the most current data is utilized for the join operation. This capability allows for more accurate and relevant results when processing real-time data alongside a stable dataset.

Stream-static joins leverage Delta Lake's ability to manage versions, so every time a new microbatch is processed, it retrieves the latest snapshot of the static data. This mechanism enhances the reliability of the operation by consistently reflecting any changes made to the static table up to the point of the next microbatch execution.

The other statements do not accurately represent the functionality of stream-static joins within Databricks. For instance, the idea that static tables cannot be used due to consistency issues is misleading, as Delta Lake is designed to provide strong consistency models even when static tables are involved. Additionally, stating that the checkpoint directory is for tracking static tables only is incorrect, as checkpoints are used in streaming in general for fault tolerance and to track progress of streaming queries, not specifically for