Apache Spark Certification Practice Test

Accumulators in Spark are specifically designed for aggregating numeric values across multiple tasks, making them particularly useful for sums and counters. They provide a simple and efficient way to implement counters, where you can collect data from various tasks during the execution of Spark jobs and aggregate those results back to the driver program. This is especially helpful in scenarios where you need to keep track of metrics such as the total number of records processed or specific counts of certain conditions met during data processing.

Accumulators are not intended for complex data transformation or storage (as suggested by the first option), nor are they primarily related to functional programming constructs like maps and filters (the second option). Additionally, while they can be utilized in both batch processing and streaming analytics, this aspect is too broad as it does not capture their primary functionality, which is focused on aggregation via sums and counters. Therefore, their main purpose is best encapsulated by the idea of performing sums and counters, making it the most accurate response.