To compare a column value with a Python literal constant in a DataFrame expression, use F.lit() to convert it into a Spark literal.

Correct refactor:

from pyspark.sql import functions as F

min_price = 110.50

result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))

This avoids type mismatches and ensures Spark executes the filter expression on the cluster.

Why the other options are incorrect:

B: where() syntax is valid, but F.lit("price") is incorrect — wraps string literal, not a column.

C: withColumn adds a column, not needed for this aggregation.

D: Comparison logic reversed.