Databricks Certified Data Engineer Professional — Question 228

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 to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

Answer options

• A. The 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.
• B. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
• C. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
• D. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
• E. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Correct answer: D

Explanation

The correct answer, D, highlights that while Databricks can infer schema, manually setting types improves data quality assurance. Other options either misrepresent how Delta Lake handles types, suggest a focus on automation over quality, or incorrectly state that inferred types will always match downstream requirements.