Which type of file can be read into a DataFrame using spark.read?

The option stating CSV and JSON files as the correct choice reflects the versatile capabilities of the Spark SQL component within Databricks for handling structured and semi-structured data formats. Spark is designed to read a variety of file formats, and CSV and JSON are among the most commonly used formats in data processing and analysis.

Both CSV (Comma-Separated Values) and JSON (JavaScript Object Notation) are standard formats used for data interchange, allowing for easy serialization of data structures. CSV files are popular for tabular data due to their straightforward structure, while JSON provides a more flexible format that can handle nested and hierarchical data.

By being able to read these formats directly into a DataFrame, Spark allows users to leverage its powerful data processing capabilities on datasets, enabling transformations, aggregations, and querying to be performed efficiently. This feature is particularly valuable in data analytics workflows, where data from different sources often comes in these formats.

In contrast, text files, household data files, and binary files either do not represent structured data well or are not directly compatible with the DataFrame API, which is designed for efficient processing of structured data. Hence, the choice that highlights the ability to read both CSV and JSON files into a DataFrame captures the essential functionality of Spark