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:
While using an EC2 Spot Instance could potentially be cost-effective, it requires continuous management and scaling, which can complicate the architecture. Additionally, it may introduce delays in processing compared to a more event-driven architecture like Lambda.
Using an AWS Lambda function to automatically convert .csv files to images upon upload is a serverless and event-driven solution, allowing for immediate processing of the files as they arrive, which meets the requirement for timely report generation.
Creating S3 Lifecycle rules to transition .csv files to S3 Glacier (which is cheaper for infrequent access) after one day and expiring image files after 30 days aligns with the requirements for cost-effectiveness, given that images are only needed temporarily while keeping .csv files for ML training.
Transitioning .csv files to S3 One Zone-IA is a reasonable option, but it does not provide the same level of cost savings as Glacier for long-term storage since the .csv files need to be retained for ML training. While it does expire images after 30 days, it does not effectively minimize costs for the retained data.
Transitioning .csv files to S3 Standard-IA may save costs but not as significantly as S3 Glacier. Keeping image files in Reduced Redundancy Storage (RRS) is also not ideal since RRS is being deprecated in favor of S3 Standard-IA and other storage classes that offer better durability.
As far as I can tell, the answer is:
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.