The best solution is Introduce Amazon ElastiCache as a caching layer to offload frequent read operations from the database. ElastiCache, particularly when using Redis or Memcached, acts as an in-memory data store that drastically improves read latency by caching the most accessed data. This approach requires minimal changes to the application logic and preserves the existing architecture, satisfying the companys need for low operational overhead and high performance.

The choice Insert Amazon RDS Proxy between the EC2 instances and the RDS database to improve connection pooling efficiency is not the right fit here. While RDS Proxy is useful for managing large numbers of short-lived connections and improving availability during failovers, it doesn't directly improve read query performance, which is the core issue in this scenario.

The option Rebuild the application on AWS Lambda to take advantage of scalability and eliminate the need for EC2 introduces a major architectural shift. Its a disruptive solution that involves a full re-architecture and doesn't address the read-heavy bottleneck at the database level. It goes beyond whats necessary to solve the problem.

The response Replace the RDS MySQL database with a DynamoDB table to gain performance at scale with serverless architecture is also overly invasive. Migrating to DynamoDB could improve performance in some contexts, but it requires rethinking the data model and modifying the application significantly, which goes against the goal of minimizing changes to the architecture.

This question is essential for assessing your understanding of performance optimization strategies in AWS, particularly how caching can offload stress from databases. A good strategy for tackling similar questions is to watch for clues like "minimize changes" or "quick improvement"these imply enhancements rather than full-scale migrations or re-architectures.

The difficulty in this question lies in identifying the simplest solution that directly targets the root cause. Many options sound technically valid but require more work or solve different problems. Choosing the one that matches both the constraints and the core issue is key.

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