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Answer: When custom logic needs to be applied at scale to array data objects
Higher-order functions in Spark SQL (such as transform(), filter(), aggregate(), and exists()) are specifically designed to operate on array data types, allowing complex operations to be applied efficiently at scale within a single row. Option C correctly identifies this primary use case. Option A is incorrect because higher-order functions are not needed for simple, unnested data where standard functions suffice. Option B is misleading as higher-order functions are already part of Spark SQL and do not require conversion to Python-native code. Option D is not a primary reason, as performance issues with built-in functions may require other optimizations. Option E is incorrect because built-in functions, including higher-order ones, already benefit from the Catalyst Optimizer.
Author: LeetQuiz Editorial Team
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When should a data analyst use higher-order functions in Spark SQL?
A
When custom logic needs to be applied to simple, unnested data
B
When custom logic needs to be converted to Python-native code
C
When custom logic needs to be applied at scale to array data objects
D
When built-in functions are taking too long to perform tasks
E
When built-in functions need to run through the Catalyst Optimizer
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