A company is designing a web application on AWS to process insurance quotes. Users request quotes. Quotes must be separated by quote type, be responded to within 24 hours, and must not be lost. The solution should maximize operational efficiency and minimize maintenance. Which solution meets these requirements?

The correct solution uses a single Amazon SNS topic with message filtering to route messages to specific Amazon SQS queues based on quote type. This design maximizes operational efficiency and minimizes maintenance by centralizing message publication while allowing for decoupled, asynchronous processing. SQS queues ensure messages are not lost and can be processed within 24 hours, even with intermittent backend availability. Kinesis Data Streams are designed for high-throughput, real-time data processing, which is overkill for this asynchronous, quote-processing scenario. AWS Lambda functions subscribed to SNS topics would work, but the question specifies "backend application servers," implying a more traditional server-based processing model, making SQS a more direct fit for queueing tasks for these servers. Kinesis Data Firehose delivering to OpenSearch Service is for analytics and search, not for reliable message queuing and task processing by backend application servers.