Preparing Practice Exam…
Please wait a moment while the system generates the exam questions, answers, and study session.
Please wait a moment while the system generates the exam questions, answers, and study session.
Databricks Certified Data Engineer Professional · Databricks
Select one answer.
Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.
- Correct answer : A — The `dlt` module is replaced by `pyspark.pipelines`, commonly imported as `dp`; existing `import dlt` pipeline code keeps running unchanged, but Databricks recommends migrating to `dp` decorators such as `@dp.table`. A . This correctly describes the rebrand: `pyspark.pipelines`, imported as `dp`, is the current recommended API, replacing the `@table`/`@view` decorators formerly imported from `dlt`, while existing `dlt`-based pipeline code continues to run without any required migration. B . The `dlt` module was not removed and existing pipelines built with `import dlt` continue to run without modification; Databricks recommends adopting the new names going forward, but no rewrite is required for old code to keep working. C . The distinction between `dlt` and `pyspark.pipelines` is not a SQL-versus-Python split; both relate to the Python API naming, and SQL pipeline definitions are a separate, unaffected syntax, so this description misstates the change entirely. D . The decorator rename did not introduce a new physical dataset type; `@dlt.table` and `@dp.table` are the old and new names for the same underlying streaming-table concept, so mixing them does not produce two independent copies of a table.