The core issue is performance degradation on a single-node Aurora cluster due to heavy read traffic from reporting queries, resulting in high CPU utilization and connection exhaustion. The most effective and native solution within the Aurora architecture is to scale out the read capacity.

Creating Aurora Replicas (Option A) allows the read-intensive reporting workload to be offloaded from the primary (writer) instance. The reader endpoint automatically load-balances connections across all available replicas. Furthermore, implementing an Aurora Auto Scaling policy based on CPU utilization ensures that the number of replicas dynamically adjusts to the workload, providing both performance and cost-efficiency. This directly addresses both the high CPU and connection limit issues by distributing the read load.

B: Creating a second, separate DB cluster is operationally complex. It requires manual setup of replication, introduces potential replication lag, and is not the intended, integrated scaling mechanism for Aurora.

C: AWS Lambda is a serverless compute service, not a caching layer. While a Lambda function could implement caching logic, it is not its primary purpose and adds unnecessary complexity.

D: While Amazon ElastiCache is a valid caching solution, it may not be effective for complex, ad-hoc reporting queries that often have a low cache-hit ratio, thus failing to significantly reduce database load.

1. Amazon Aurora User Guide - Aurora Replicas: "You can distribute the read workload for your Aurora DB cluster by adding Aurora Replicas. Aurora Replicas are independent endpoints in an Aurora DB cluster

best used for scaling read operations and increasing availability... Aurora Replicas connect to the same shared storage volume as the primary DB instance." (Source: AWS Documentation

Amazon Aurora User Guide

"Amazon Aurora DB clusters"

Section: "Aurora Replicas").

2. Amazon Aurora User Guide - Connection Management: "For read-only connections

you can use the reader endpoint. This endpoint provides load balancing for read-only connections to the Aurora DB cluster... The reader endpoint load-balances connections to available Aurora Replicas in an Aurora DB cluster." (Source: AWS Documentation

Amazon Aurora User Guide

"Amazon Aurora connection management"

Section: "Aurora endpoints").

3. Amazon Aurora User Guide - Aurora Auto Scaling: "Amazon Aurora Auto Scaling enables an Aurora DB cluster to automatically adjust the number of Aurora Replicas in response to changes in your workload... You can base the scaling policy on a target value for a predefined metric

such as average CPU utilization or average connections." (Source: AWS Documentation

Amazon Aurora User Guide

"Managing an Amazon Aurora DB cluster"

Section: "Using Amazon Aurora Auto Scaling with Aurora Replicas").