Explanation:
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.
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