B is the right answer as Option B is more typical for stream-static joins, as it provides a consistent static DataFrame snapshot for the entire job's duration. Option A might be suitable in specialized cases where you need real-time updates of the static DataFrame for each microbatch.
Answer is A, When Azure Databricks processes a micro-batch of data in a stream-static join, the latest valid version of data from the static Delta table joins with the records present in the current micro-batch
from https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/delta-lake
This is straight from docs, "A stream-static join joins the latest valid version of a Delta table (the static data) to a data stream using a stateless join.
When Azure Databricks processes a micro-batch of data in a stream-static join, the latest valid version of data from the static Delta table joins with the records present in the current micro-batch. Because the join is stateless, you do not need to configure watermarking and can process results with low latency. The data in the static Delta table used in the join should be slowly-changing."
https://learn.microsoft.com/en-us/azure/databricks/transform/join#stream-static
Correct answer is A. https://docs.databricks.com/en/structured-streaming/delta-lake.html
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