Which combination of steps will meet these requirements MOST cost-effectively?
(Choose two.)
Launch an Amazon EC2 Spot Instance that downloads the .csv files every hour, generates the image files, and uploads the images to the S3 bucket.
Design an AWS Lambda function that converts the .csv files into images and stores the images in the S3 bucket. Invoke the Lambda function when a .csv file is uploaded.
Create S3 Lifecycle rules for .csv files and image files in the S3 bucket. Transition the .csv files from S3 Standard to S3 Glacier 1 day after they are uploaded. Expire the image files after 30 days.
Create S3 Lifecycle rules for .csv files and image files in the S3 bucket. Transition the .csv files from S3 Standard to S3 One Zone-Infrequent Access (S3 One Zone-IA) 1 day after they are uploaded. Expire the image files after 30 days.
Create S3 Lifecycle rules for .csv files and image files in the S3 bucket. Transition the .csv files from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) 1 day after they are uploaded. Keep the image files in Reduced Redundancy Storage (RRS).
Explanations:
Launching an EC2 Spot Instance for continuous processing every hour may incur higher costs due to the instance running and the management overhead. Lambda is a more cost-effective solution for event-driven processing.
Using an AWS Lambda function to automatically convert .csv files to images upon upload is efficient and cost-effective, as Lambda only incurs costs when the function is executed, and it scales automatically.
Implementing S3 Lifecycle rules to transition .csv files to S3 Glacier after 1 day optimizes storage costs, while expiring image files after 30 days ensures they are removed when no longer needed, aligning with the requirements.
Transitioning .csv files to S3 One Zone-IA saves costs, but not as effectively as Glacier for long-term storage, which is necessary since these files need to be retained for ML training. The expiration of images is handled correctly.
Transitioning .csv files to S3 Standard-IA may not be as cost-effective as Glacier for the intended use case of retaining files for ML training. Additionally, using Reduced Redundancy Storage (RRS) for images is not ideal as it does not provide the same durability as standard storage classes.