Option B is the correct answer because it leverages Amazon SQS to handle event queuing and an Amazon EC2 Auto Scaling group to dynamically scale the processing capacity based on the specific SQS queue metric ApproximateAgeOfOldestMessage. Here’s the detailed breakdown:

1. Scalability:

   • Using an Amazon EC2 Auto Scaling group is a robust approach for scaling the number of processing instances. The scaling policy based on ApproximateAgeOfOldestMessage ensures that the number of instances adjusts according to the workload. This metric gives a reliable indication of backlog in the queue, which directly correlates to the need for more or fewer processing resources.

2. Error Handling:

   • Amazon SQS dead-letter queues (DLQs) provide a straightforward mechanism to handle processing errors. If a message fails to be processed after a specified number of attempts, it is automatically moved to the dead-letter queue. This isolation of failed messages facilitates easy review and debugging.

3. Decoupling and Reliability:

   • SQS decouples the event producers and consumers, enhancing the overall architecture’s resilience and fault tolerance. The producers (publishing events to SQS) do not need to worry about the consumers’ processing capacity, as SQS handles the load balancing and retry logic implicitly.

This approach offers a highly scalable, resilient, and manageable solution for processing events and handling errors effectively, which aligns well with the best practices for building scalable and fault-tolerant applications on AWS.