AWS Certified Solutions Architect – Professional (SAP-C02) — Question 149

A company is running an event ticketing platform on AWS and wants to optimize the platform's cost-effectiveness. The platform is deployed on Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 and is backed by an Amazon RDS for MySQL DB instance. The company is developing new application features to run on Amazon EKS with AWS Fargate.

The platform experiences infrequent high peaks in demand. The surges in demand depend on event dates.

Which solution will provide the MOST cost-effective setup for the platform?

Answer options

• A. Purchase Standard Reserved Instances for the EC2 instances that the EKS cluster uses in its baseline load. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet predicted peak load for the year.
• B. Purchase Compute Savings Plans for the predicted medium load of the EKS cluster. Scale the cluster with On-Demand Capacity Reservations based on event dates for peaks. Purchase 1-year No Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale out database read replicas during peaks.
• C. Purchase EC2 Instance Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.
• D. Purchase Compute Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

Correct answer: D

Explanation

Option D is the most cost-effective solution as it uses Compute Savings Plans to handle the predictable base load while leveraging Spot Instances for cost savings during peak demands. The other options either don't optimize costs as effectively or involve unnecessary upfront commitments that don't align with the infrequent peak usage.