What change will resolve the capacity problem in the Azure Function processing loop?

Modifying the loop to process items in parallel (PC09) is the correct solution because it directly addresses the bottleneck of sequential processing. The current code processes each item one by one, which is inefficient for large datasets and leads to capacity issues. Parallel processing allows multiple items to be handled concurrently, significantly improving throughput and reducing the overall processing time. Switching to an Azure Blob storage trigger might change how the function is invoked but doesn't resolve the internal processing bottleneck. Verifying the consumption plan configuration is important for scaling, but if the code itself is inefficient, scaling alone won't fully fix the problem. Moving to a dedicated App Service Plan provides more resources but doesn't optimize the code's execution logic.