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Answer: Implement a Lambda function for .csv to image conversion, triggered by new .csv uploads to S3., Set S3 Lifecycle rules to transition .csv files to Glacier after one day, and expire images after 30 days.
The most cost-effective solution leverages AWS Lambda functions and S3 Lifecycle rules. Option B suggests using AWS Lambda to convert .csv files to images upon upload, which is a cost-effective and scalable way to handle on-the-fly processing without the need for continuously running EC2 instances. Option C suggests using Lifecycle rules to move .csv files to Glacier (a low-cost storage option) after one day and expiring image files after 30 days, which optimizes storage costs. Therefore, the correct options are B and C.
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A company's sensors upload .csv files to an Amazon S3 bucket, requiring conversion to images for immediate graphical report generation. How to cost-effectively manage file storage and conversion with the following constraints: images are irrelevant after 1 month, .csv files are needed for semi-annual ML model training?
A
Use an EC2 Spot Instance to process .csv files hourly, generating images uploaded to S3.
B
Implement a Lambda function for .csv to image conversion, triggered by new .csv uploads to S3.
C
Set S3 Lifecycle rules to transition .csv files to Glacier after one day, and expire images after 30 days.
D
Set S3 Lifecycle rules to transition .csv files to One Zone-IA after one day, expiring images after 30 days.
E
Set S3 Lifecycle rules to transition .csv files to Standard-IA after one day, using RRS for image files.