Apache Spark Certification Practice Test

The correct response highlights the modes utilized for executing independent batch jobs in Spark, particularly focusing on the distinct operational characteristics that define these environments. Batch mode refers to the operation of Spark jobs that process data in discrete chunks or batches, typically from sources like Hadoop Distributed File System (HDFS) or other storage systems. This is beneficial for processing large volumes of data that do not need immediate real-time processing.

Customer mode does not represent a standard operational mode within the Spark framework. Instead, the well-defined terms in Spark include standalone mode, cluster mode, and others that more accurately describe how Spark interacts with its environment and resource management.

In the context of batch processing frameworks, the term "batch mode" aptly describes the set of operations for handling independent jobs, while "customer mode" is not an established term within Spark’s operational lexicon. Therefore, while the intuitive thinking might lead one to associate these terms in a general sense, they do not align correctly with Spark’s specification of job modes, and thus "Customer mode" does not contribute accurately to the available modes for independent batch jobs.

The other possible options also reflect inaccuracies regarding definitions or pairings of modes used in Spark. Recognizing that only batch mode retains accuracy in representing the independent batch job