Which combination of steps will meet these requirements?
(Choose two.)
Change all the EC2 instances to compute optimized instances that have the same number of cores as the existing EC2 instances.
Move the application frontend to a static website that is hosted on Amazon S3.
Deploy the application frontend by using AWS Elastic Beanstalk. Use the same instance type for the nodes.
Change all the backend EC2 instances to Spot Instances.
Deploy the backend Python application to general purpose burstable EC2 instances that have the same number of cores as the existing EC2 instances.
Explanations:
Changing to compute optimized instances may not reduce costs significantly since they are often more expensive and do not address the application’s usage pattern of low traffic outside peak hours. The instance type should reflect the actual workload requirements rather than just matching cores.
Moving the frontend to a static website hosted on Amazon S3 significantly reduces costs as S3 is a cheaper storage solution for static content. It also improves scalability and availability, eliminating the need for running EC2 instances for the frontend.
Using AWS Elastic Beanstalk does not inherently reduce costs. It may add management overhead and does not address the application’s traffic pattern. Additionally, maintaining EC2 instances for the frontend is not as cost-effective as moving to S3.
Switching backend EC2 instances to Spot Instances may introduce availability risks, especially during peak usage when instances might be terminated if spot prices rise. This approach could negatively impact application availability, which is a concern in this scenario.
Deploying the backend Python application on general purpose burstable EC2 instances (such as T3 instances) allows for cost savings due to lower pricing, especially since the application experiences low traffic outside of peak usage. This aligns with the need to optimize costs without affecting availability.