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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Question # 56 Topic 6 Discussion

Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Question # 56 Topic 6 Discussion

Databricks-Certified-Data-Engineer-Associate Exam Topic 6 Question 56 Discussion:
Question #: 56
Topic #: 6

A data engineer is designing a Bronze-to-Silver pipeline on the Databricks Data Intelligence Platform. The source system sends daily CSV files, and new optional columns are added over time.

The engineer needs a storage format and table capabilities that provide all of the following:

    Writes that do not conform to the defined schema are rejected.

    The schema can evolve to include new optional columns without manually recreating the table.

    Previous table versions can be queried for debugging and auditing.

Which solution fulfills these requirements?


A.

Use a Parquet table with Spark’s default schema inference and rerun the job whenever the schema changes.


B.

Use a Delta table with schema enforcement and recreate the table whenever new columns are added.


C.

Use an external table with Auto Loader schema inference for the CSV files.


D.

Use a Delta table with its native schema enforcement, schema evolution, and table-history capabilities.


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