Every day at 1 AM, all the ATM transactions at ABC Bank from the previous...

Comprehensive and Detailed Explanation From Exact Extract:

Pega’s integration and data processing capabilities, as taught in Pega Academy’sIntegration MissionandLead System Architect Mission, emphasize scalable, automated solutions for handling large datasets. For processing high-volume ATM transaction data, the solution must support efficient file ingestion and processing.

Option A (Incorrect): Advanced agents are legacy components and not recommended for high-volume data processing due to their limited scalability and lack of fault tolerance. Pega discourages their use for new implementations, per theAsynchronous Processingmodule.

Option B (Incorrect): Queue Processors are designed for processing individual items or events, not bulk file ingestion. Processing millions of transactions fromExcel files via Queue Processors would be inefficient and resource-intensive, as noted in theQueue Processorsection.

Option C (Incorrect): Job Schedulers are used for recurring tasks but are not optimized for processing large files directly. They lack the native file-handling capabilities of Data Sets and Data Flows, making them suboptimal, per theJob Scheduler Configurationmodule.

Option D (Correct): Using a File Data Set to ingest Excel files from a NAS directory and feeding them into a Data Flow is the optimal solution. File Data Sets are designed for bulk file processing, and Data Flows efficiently handle large volumes of data, such as millions of ATM transactions. This approach supports scalability and automation, aligning with Pega’s best practices in theData Set and Data Flowmodules.