Answer B is correct:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support-matrix-for-joins-in-streaming-queries
When we take a look in the supported join matrix between static and stream inputs, we can identify, that Stream-Static + outer is not supported.
Answer E is wrong, because the Static-Stream + right join is supported.
Answer B. We can directly discard options C and D since the streaming DataFrame (streamingDF) must be the left table in the join and the join type must be inner join or left outer join. Thus, the most directly invalid code block due to an unsupported join type is B.
B.
We match all the records from a static DataFrame on the left with a stream DataFrame on the right. If records do not match from the static DF (Left) to stream DF (Right), then the system cannot return null since the data changes on stream DF (Right), and we cannot guarantee if we will get matching records. That is why full_outer join is not supported.
Specifically, outer joins are not supported with a static DataFrame on the right and a streaming DataFrame on the left. This is because it’s not possible to guarantee all necessary rows will be available in the streaming DataFrame for every micro-batch.
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