Databricks Fundamentals 2025 – 400 Free Practice Questions to Pass the Exam

The command to load data from a CSV file into a DataFrame in Databricks is written as `spark.read.csv("path/to/file.csv")`. This syntax leverages the Spark session, which provides a unified entry point for reading data. The `spark` object refers to the active Spark session, and `read` is a method that allows you to access various data formats. By chaining `.csv()`, you specify the format of the data you want to load, in this case, CSV.

This command also allows for additional options, such as specifying delimiters, header presence, and more, making it very versatile for different CSV file structures. Using this standardized approach ensures that you can efficiently load data while taking advantage of Spark's capabilities for data processing and transformation.

Other options do not conform to the proper methods and structure required by the Spark API for reading CSV files. For example, using `load.csv` or `spark.read.dataframe` would not recognize these as valid commands for reading CSV files, as there are specific methods designated for handling different data formats in Spark. Thus, understanding the structure and available methods properly leads to the correct command for loading CSV data.