The best answer is: Set up an Amazon SNS topic and use Amazon SQS queues subscribed to that topic. Use message filtering to direct different quote types to the appropriate SQS queue, with each backend processor polling from its designated queue.

This solution combines Amazon SNS and SQS to build a scalable, loosely coupled, and highly available messaging architecture. SNS enables message fan-out, and SQS ensures that messages are durably stored until processed. By applying message filtering on the SNS topic, each SQS queue receives only the relevant messages for its quote type. This approach guarantees message durability (ensuring no data loss), supports asynchronous processing within the 24-hour window, and requires minimal operational overhead thanks to fully managed services.

The option Deploy an AWS Lambda function and an SNS topic for each quote type introduces more complexity and does not provide guaranteed durability of unprocessed messages. If the Lambda function fails or is throttled, messages may be lost unless additional retry logic or dead-letter queues are implemented, which increases maintenance effort.

The option Use Kinesis data streams for each quote type is more suited for real-time data ingestion and analytics, not for message durability across a 24-hour SLA. It also requires developers to manage shard scaling and checkpointing via the Kinesis Client Library, which adds operational complexity.

The option Create Kinesis Data Firehose delivery streams for each quote type and use OpenSearch Service to store and retrieve messages is a misuse of these services. Firehose is optimized for writing data into storage destinations, not for message queuing and processing. OpenSearch is not a message queue and does not offer durability guarantees suitable for transactional workloads like insurance quotes.

This question is important because it assesses your ability to build a resilient, event-driven architecture on AWS that separates concerns, handles message routing, and maintains delivery guarantees. When you see requirements like 'must not get lost' and 'must process within a timeframe,' consider solutions like SQS that offer durability, decoupling, and visibility into processing workflows.

A good trick for answering similar questions is to recognize SNS + SQS as a common design pattern for scalable, categorized message delivery with filtering support. Avoid real-time streaming or search-based services when the problem centers around durability and delayed asynchronous processing.

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