Apache Spark Certification 2025 – 400 Free Practice Questions to Pass the Exam

In Apache Spark, a transformation is an operation that produces a new dataset from an existing one. Transformations are lazy operations, meaning that they are not executed immediately but rather set up a lineage of operations to be performed when an action is called.

The operation that is identified as a transformation in this context is the map function. The map transformation takes a function as input and applies it to each element of the dataset, resulting in a new dataset composed of the results. This is a fundamental operation in functional programming and is commonly used in Spark to perform data processing tasks in a distributed manner.

In contrast, the count, collect, and show operations are classified as actions. Actions trigger the execution of the transformations that have been defined on the dataset and return a value or output to the driver program rather than creating a new dataset. For instance, count returns the number of elements in the dataset, collect retrieves all the elements and brings them to the driver as an array, and show displays a limited number of elements in the dataset to the console. Understanding the difference between transformations and actions is crucial for effectively utilizing Spark's capabilities in data processing workflows.