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.