A is the correct answer.

Explanation:

In Databricks (Spark Structured Streaming + Delta Lake), a stream-static join is stateless. When the static side is a Delta table, each micro-batch of the streaming data is joined against the latest valid version of that Delta table as of the time that micro-batch is processed.

Key points from official behavior:

• The join uses the most recent committed version of the static Delta table for every micro-batch.
• This allows the streaming job to automatically pick up updates/inserts/deletes made to the static Delta table without needing to restart the streaming query.
• No watermarking is required (stateless join).
• The checkpoint directory is not used to track versions of the static table or join state (the streaming side still uses checkpointing for its own fault tolerance, but the static side is re-read fresh per micro-batch).

Why the other options are incorrect:

• B: Wrong — it does not freeze at job initialization. It uses the latest version per micro-batch.
• C: Incorrect — stream-static joins are stateless; no join state (unique keys) is tracked in the checkpoint.
• D: False — stream-static joins work very well with static Delta tables (this is a recommended pattern).
• E: Incorrect — the checkpoint does not track updates to the static table.

This is the standard documented behavior for stream-static joins with Delta tables in Databricks.