Exam Certified Data Engineer Professional topic 1 question 148 discussion

The purpuse of it is to take into account changees made manually in the raw_iot table and propagate them to the downstream tables.

Answer is B. Ignore updates and deletes - https://docs.databricks.com/en/structured-streaming/delta-lake.html#ignore-updates-and-deletes Structured Streaming does not handle input that is not an append and throws an exception if any modifications occur on the table being used as a source. There are two main strategies for dealing with changes that cannot be automatically propagated downstream: You can delete the output and checkpoint and restart the stream from the beginning. You can set either of these two options: ignoreDeletes: ignore transactions that delete data at partition boundaries. skipChangeCommits: ignore transactions that delete or modify existing records. skipChangeCommits subsumes ignoreDeletes.

Setting pipelines.reset.allowed property to false on raw_iot prevents its full refresh/reset being done from its source, meaning it preserves all other modifications done for it additionally outside of its source streaming process. If we set skipChangeCommits for it or the target table transactions that delete or modify records on the table are ignored

B. Set the skipChangeCommits flag to true on raw_iot Let's break down the requirements and explain why this is the best solution: Retain manually deleted or updated records in raw_iot: The skipChangeCommits flag, when set to true, tells Delta Live Tables (DLT) to ignore any manual changes (updates or deletes) made to the table outside of the pipeline. This means that even if records are manually deleted or updated in the raw_iot table, these changes won't be reflected in the table when the pipeline runs again. Recompute downstream bpm_stats table: By default, DLT will recompute downstream tables when their upstream dependencies change. Since bpm_stats is based on raw_iot, it will naturally be recomputed when the pipeline updates, without any special configuration. Why the other options are not correct: A. Setting pipelines.reset.allowed to false on raw_iot would prevent the table from being reset, but it wouldn't address the requirement to retain manually deleted or updated records.

B. Set the skipChangeCommits flag to true on raw_iot Let's break down the requirements and explain why this is the best solution: Retain manually deleted or updated records in raw_iot: The skipChangeCommits flag, when set to true, tells Delta Live Tables (DLT) to ignore any manual changes (updates or deletes) made to the table outside of the pipeline. This means that even if records are manually deleted or updated in the raw_iot table, these changes won't be reflected in the table when the pipeline runs again. Recompute downstream bpm_stats table: By default, DLT will recompute downstream tables when their upstream dependencies change. Since bpm_stats is based on raw_iot, it will naturally be recomputed when the pipeline updates, without any special configuration. Why the other options are not correct: A. Setting pipelines.reset.allowed to false on raw_iot would prevent the table from being reset, but it wouldn't address the requirement to retain manually deleted or updated records.

answer A This property, when set to false, ensures that the table will not be reset during pipeline updates, thus preserving manually deleted or updated records. This is crucial for the raw_iot table to retain the manual modifications.

Set the pipelines.reset.allowed property to false on raw_iot

Answer: B