Apache Spark Certification Practice Test

In Apache Spark, transformations are operations that create a new dataset from an existing one, applying a function or process to each element of the dataset. The correct choices that represent transformations would typically include operations such as Filter, Map, and Join.

Transformation functions generally maintain data lineage, which allows Spark to keep track of how data is derived, enabling it to optimize execution and handle faults effectively.

The Map transformation, for instance, applies a given function to each item in the dataset, resulting in a new dataset that contains modified elements based on specified logic. Similarly, Join combines datasets based on key fields, effectively transforming two datasets into one.

In contrast, the option labeled as Random does not function as a transformation within the context of Spark. Instead, it may refer to sampling or generating random data, which does not modify an existing dataset in the same way as the recognized transformations.

Thus, Filter and Map are both clear examples of transformations, while Join is another valid transformation. Each of these plays an important role in the processing pipeline of data within Spark.