Which of the following workloads are best suited for autonomous transaction processing?

Autonomous transaction processing is particularly well-suited for transactions because it involves handling a large number of simultaneous requests efficiently and reliably. In a transaction processing environment, operations such as banking transactions, online bookings, or inventory updates require quick and accurate processing with minimal human intervention.

Autonomous transaction processing systems leverage technologies like artificial intelligence and machine learning to optimize performance, manage workloads, and ensure data integrity. This capability allows for a high degree of automation to execute complex transaction workflows while maintaining the consistency and reliability required for real-time, transactional environments.

In contrast, while batch processing involves processing large volumes of data at once, it does not require the same level of immediacy and continuous operation as transaction processing. Data warehouse analytics focuses on querying and analyzing larger datasets rather than conducting real-time transactions. High-performance computing typically involves complex calculations and simulations, which differ fundamentally from the transactional processes oriented around discrete events or operations. Hence, the nature of autonomous transaction processing aligns best with managing and executing transactions efficiently.