Which feature would you utilize to ensure optimal performance during high volumes of requests?

Batch Processing is the correct choice for ensuring optimal performance during high volumes of requests. This feature allows you to collect and process data in large batches rather than processing each request individually in real time. When dealing with a high volume of requests, this approach minimizes the overhead associated with individual request handling, such as context switching and resource allocation.

By grouping requests together, Batch Processing can efficiently utilize resources, reduce the frequency of API calls, and improve throughput. It is particularly useful when the data can be processed asynchronously, allowing systems to handle spikes in demand without overwhelming the infrastructure.

In contrast, Real-Time Processing, while essential for immediate data handling, may not perform as efficiently during high volumes since it processes each request as it comes in. Legacy Processing often refers to traditional methods that might not be optimized for modern demand. Inline Processing, similar to Real-Time, handles requests in a sequential manner, which can lead to performance bottlenecks during peak times.

In summary, Batch Processing allows the system to manage high volumes more effectively, making it the best option for ensuring optimal performance.