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.

Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert a pyspark.pandas.DataFrame to a standard PySpark DataFrame, you use .to_spark().

Example:

df = psdf.to_spark()

This is the officially supported method as per Databricks Documentation.

Incorrect options:

B, D: Invalid or nonexistent methods.

C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

In Structured Streaming, a watermark defines the maximum delay for event-time data to be considered in stateful operations like deduplication or window aggregations.

Behavior:

df = df.withWatermark("event_timestamp", "30 minutes")

This sets a 30-minute watermark, meaning Spark will only keep track of events that arrive within 30 minutes of the latest event time seen so far. When used with:

df.dropDuplicates(["event_id", "event_timestamp"])

Spark removes duplicates that arrive within the watermark threshold (in this case, within 30 minutes).

Why other options are incorrect:

A: Watermarks do not remove all duplicates; they only manage those within the defined event-time window.

B: Watermark durations can be expressed as strings like "30 minutes", "10 seconds", etc., not only seconds.

D: Structured Streaming supports deduplication using withWatermark() and dropDuplicates().

References (Databricks Apache Spark 3.5 – Python / Study Guide):

PySpark Structured Streaming Guide — withWatermark() and dropDuplicates() methods for event-time deduplication.

Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section “Structured Streaming” — Topic: Streaming Deduplication with and without watermark usage.