Which programming model is used to process large data sets by dividing tasks across multiple parallel systems?

The correct choice is MapReduce because it specifically refers to a programming model designed to handle large-scale data processing by dividing tasks into smaller components that can be processed in parallel across a distributed system. In this model, the "Map" phase involves distributing data and tasks across multiple nodes for processing, while the "Reduce" phase aggregates the results from these nodes, ultimately producing a consolidated output.

MapReduce is particularly effective for working with vast amounts of data, making it a foundational component in many big data frameworks, such as Apache Hadoop. This model is optimized for situations where data sets are too large to fit into a single machine's memory and requires distributing the workload across multiple machines to achieve both efficiency and speed.

The other options, while related to data processing, do not fit the description as precisely as MapReduce. Parallel processing is a broader term that describes the simultaneous execution of tasks but doesn’t specifically imply the structured, divided approach of MapReduce. Data mining refers to extracting useful patterns from large sets of data but does not inherently involve the parallel processing aspect. Batch processing involves executing a series of jobs in a group without real-time consideration, which lacks the dynamic parallelism and task division that MapReduce offers.