Databricks Certified Data Engineer Professional Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  ,B - Correct answer s : A, B — `AUTO CDC INTO` consumes a continuous stream of row-level events, such as insert, update, and delete records emitted by a CDC log or message bus.; `AUTO CDC FROM SNAPSHOT` is designed for sources that periodically expose a full table snapshot rather than a stream of discrete change events. A . `AUTO CDC INTO` is the API built around an incoming stream of discrete change events, matching sources like a CDC log or a message bus that emit one record per insert, update, or delete. B . `AUTO CDC FROM SNAPSHOT` targets sources that only ever expose a full point-in-time snapshot rather than a change feed, and it works by diffing successive snapshots to derive the effective inserts, updates, and deletes. C . The availability is reversed from this claim: `AUTO CDC INTO` is supported in both SQL and Python, while `AUTO CDC FROM SNAPSHOT` is the one restricted to the Python interface only. D . An explicit operation-type column is only needed to identify which rows represent deletes via `APPLY AS DELETE WHEN`; ordinary insert and update rows do not require such a column, so this is not a universal requirement of every incoming row. E . Tables produced by Lakeflow pipelines, including those written by either CDC API, are Delta tables managed as Unity Catalog streaming tables; external Iceberg access is an optional add-on via table properties, not a restriction that excludes Unity Catalog governance.