Answer: Spark DataFrames are immutable by default.

Spark DataFrames are immutable by default, which is a core design principle offering several benefits: - **Fault Tolerance**: Simplifies recovery from failures since the original DataFrame remains unchanged. - **Parallelism**: Enables independent processing of DataFrame parts without modification conflicts. - **Reproducibility**: Ensures consistent outcomes from the same operations on the original DataFrame. Incorrect options explained: - **A & C**: There's no support for in-place modifications or an `inplace=True` parameter, as these would violate immutability. - **B**: Mutability is not the default; immutability is fundamental to Spark's design.

Author: LeetQuiz Editorial Team