The following code fragment results in an error:@F.

Comprehensive and Detailed Explanation:

The original code has several issues:

It references a variable answer that is undefined.

The function is annotated to return a str, but the logic attempts numeric multiplication.

The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.

Option B correctly:

Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).

Declares the input as float, which aligns with the multiplication.

Returns a float, which matches both the logic and the schema type annotation.

This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:

"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)."

Example from official documentation:

from pyspark.sql.functions import udf

from pyspark.sql.types import DoubleType

@udf(returnType=DoubleType())

def multiply_by_pi(x: float) -> float:

return x * 3.14159

This makes Option B the syntactically and semantically correct choice.