Hospital XYZ wants to analyze patient data and identify those at high risk of readmission.

Comprehensive and Detailed Explanation From Exact Extract:

Pega’s Data Flows and Queue Processors serve distinct purposes in asynchronous processing, as explained in Pega Academy’sLead System Architect Missionand thePega Certified Lead System Architect Study Guide. Data Flows are designed for processing large datasets, often involving multiple sources, while Queue Processors handle individual tasks.

Option A (Correct): Data Flows can directly access data from multiple systems using datasets (e.g., connectors to EHR and admission systems). This makes them ideal for aggregating and analyzing data from disparate sources, as documented in theData Flow Configurationmodule.

Option B (Correct): Data Flows are optimized for handling large volumes of data, such as patient records, through batch or stream processing. They scale efficiently for big data scenarios, unlike Queue Processors, which focus on individual items, per theData Flowsection in Pega Community.

Option C (Incorrect): Data Flows do not inherently prioritize tasks based on urgency. Prioritization is typically managed by Queue Processors or SLAs, not Data Flows, as noted in theAsynchronous Processingmodule.

Option D (Incorrect): While Data Flows can process data in near real-time via streaming, “real-time” processing is more characteristic of Queue Processors for immediate, single-item tasks. Data Flows are better suited for continuous or batch processing, per theData Flow Configurationmodule.