Databricks Certified Data Engineer Professional Exam Databricks-Certified-Professional-Data-Engineer Question # 16 Topic 2 Discussion

Databricks Certified Data Engineer Professional Exam Databricks-Certified-Professional-Data-Engineer Question # 16 Topic 2 Discussion

Databricks-Certified-Professional-Data-Engineer Exam Topic 2 Question 16 Discussion:
Question #: 16
Topic #: 2

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.

The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.

Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?


A.

Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.


B.

Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.


C.

Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.


D.

By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.


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