Which combination of actions should a solutions architect take to OPTIMIZE costs for this application?
(Choose two.)
Migrate the application to run an AWS Lambda function. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule the Lambda function to run once each month.
Configure the application to download the source files by using streams. Direct the streams into a compression library. Direct the output of the compression library into a target object in Amazon S3.
Configure the application to download the source files from Amazon S3 and save the files to local storage. Compress the files and upload them to Amazon S3.
Configure the application to run as a container in AWS Fargate. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule the task to run once each month.
Provision an Amazon Elastic File System (Amazon EFS) file system. Attach the file system to the AWS Lambda function.
Explanations:
AWS Lambda has a maximum execution time of 15 minutes, which is insufficient for processing 1 TB of data in 2 hours. Additionally, Lambda is not suitable for large file manipulation in this case.
By streaming the data directly from S3 to compression, the application avoids using local storage, saving costs on storage and potentially speeding up the process.
Storing files locally on EC2 before compression adds unnecessary storage costs, especially considering the large size of the data (1 TB). This approach does not optimize costs.
Running the application in AWS Fargate eliminates the need for managing EC2 instances and associated costs. Fargate can scale on-demand, making it more cost-efficient.
Amazon EFS is typically more expensive than S3 for storage, and is not optimized for this use case. Also, Lambda doesn’t need EFS for this type of workload.
I estimate that the answer is:
Configure the application to download the source files by using streams. Direct the streams into a compression library. Direct the output of the compression library into a target object in Amazon S3.