Free Databricks Certified Data Engineer Associate Actual Exam Questions - Question 9 Discussion

A sounds off since array functions focus on arrays specifically, not multiple data types at once. D still fits better if we think about JSON’s nested arrays, right? Could the question be hinting at something else though?

Probably D. Array functions are designed to manipulate nested data types like arrays, which are common in JSON files, so they fit best with complex, nested data handling.

D. Array functions in Spark SQL are mostly about handling complex nested data like arrays, which often come from JSON or similar sources. They don’t really deal with partitions/windows (B) or time intervals (C). E sounds off since it’s about tables, not arrays. And A is too broad—array functions focus on array-type columns specifically, not all data types at once.

Maybe D, because array functions mainly help handle nested arrays, not broad data types.

It’s D because array functions specifically help manipulate nested array data structures, not broad data types.

D, since array functions target complex nested structures, not just data types.

The key here is that Spark’s array functions let you manipulate arrays regardless of how they’re ingested, not just JSON. So D seems most fitting since it covers complex nested data, which arrays handle well. D

I’m pretty sure it’s not E since Spark doesn’t treat tables like arrays for automation in SQL. Also, C and B seem off because those are more about time windows and partitions, not arrays. A sounds too broad and vague. So the best fit is D — Spark’s array functions are mainly for working with complex nested data structures like JSON arrays, handling them smoothly inside SQL queries.

Option D seems closest, but does Spark SQL’s array functions specifically target nested JSON data, or just handle arrays in general? Wondering if the question means the general array support or JSON parsing specifically.