AWS Certified CloudOps Engineer Associate SOA-C03 Practice Question

A company runs a production Amazon RDS for PostgreSQL instance that handles both read and write traffic. At month end, CPU utilization exceeds 90% for several hours, degrading performance. The operations team wants a solution that automatically scales compute capacity up and down within minutes, maintains a single writer endpoint, and charges only for actual usage. Which approach meets these requirements with the least operational overhead?

Add multiple Amazon RDS read replicas and distribute traffic through an Application Load Balancer to absorb the extra load.
Create AWS Lambda functions triggered by CloudWatch alarms to modify the DB instance class and revert it after the spike.
Migrate the database to an Amazon Aurora Serverless v2 PostgreSQL-compatible cluster and use its built-in automatic scaling.
Enable Amazon RDS Storage Auto Scaling on the existing instance to increase compute capacity when CPU exceeds a threshold.

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Answer Description

Migrating the database to an Amazon Aurora Serverless v2 PostgreSQL-compatible cluster satisfies every requirement. Aurora Serverless v2 automatically and rapidly adjusts the number of Aurora capacity units in fine-grained increments based on workload, so it can react within seconds to the month-end spike and then scale back when demand subsides. The cluster presents a single writer endpoint, so application connectivity does not change, and billing is based on the capacity actually consumed. Storage Auto Scaling only adds storage, not compute. Manually changing the DB instance class (even through automation) incurs downtime and maintenance effort. Adding read replicas or placing them behind a load balancer can relieve read pressure but does not increase write throughput or CPU capacity on the primary instance, and it introduces replication lag and additional management.

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