Apache Spark Certification Practice Test

As of February 2015, Apache Spark indeed introduced support for DataFrames, making the answer accurate. DataFrames were a significant addition to Spark's capabilities, allowing users to work with structured data in a distributed manner similar to working with a table in a relational database or a DataFrame in Python's pandas library. This inclusion was aimed at simplifying data processing by providing a higher-level abstraction over the RDD (Resilient Distributed Dataset) API, enabling users to leverage both SQL-like queries and complex analytics in a unified interface.

The introduction of DataFrames in Spark allowed better optimization under the hood with the Catalyst optimizer, enhancing the performance of queries compared to operating solely on RDDs. This feature was part of Spark 1.3.0, which was released around that time, highlighting the evolution of Spark towards handling larger datasets and more complex data processing needs.