Apache Spark Certification Practice Test

The correct approach to spread an RDD named 'data' across 8 nodes in Python is to use the method that allows you to specify both the data and the number of partitions. In this case, using sc.parallelize(data, 8) enables the creation of an RDD from existing data while defining that the resulting RDD should be distributed into 8 partitions.

Partitioning is crucial in distributed computing because it allows Spark to effectively distribute the workload across multiple nodes, enabling parallel processing. By specifying the number of partitions, you're instructing Spark to split the data so that it can be processed more efficiently across the available resources.

Other options do not fit as effectively for creating a distributed RDD with a specific partition count. For instance, using just sc.parallelize(data) creates an RDD but relies on the default number of partitions, which may not utilize all resources effectively. The option that includes repartitioning implies modifying an RDD already created but does not apply in this context of creating a new RDD from the data. The term 'distribute' is not a recognized method in Spark's API for achieving this task, which makes that option invalid. Therefore, sc.parallelize(data, 8) correctly achieves the desired outcome