Free AWS DVA-C02 Actual Exam Questions - Question 4 Discussion
devices with unique screen dimensions and resolutions. Photos are stored in Amazon S3 in their
original format and resolution. The company uses an Amazon CloudFront distribution to serve the
photos The app includes the dimension and resolution of the display as GET parameters with every
request.
A developer needs to implement a solution that optimizes the photos that are served to each device
to reduce load time and increase photo quality.
Which solution will meet these requirements MOST cost-effective?
I think C could be a solid pick here. Doing real-time optimization via Lambda@Edge means no need to store tons of variants upfront, which saves on storage costs. If you max out the CloudFront cache TTL, most requests hit the cache after the first, reducing repeated Lambda invocations and keeping latency down. It’s cost-effective since you avoid both large S3 storage and batch processing costs. Plus, it handles unpredictable device dimensions well without pre-generating variants. So I’d go with C for balancing flexibility and cost.
Maybe B makes sense too since it pre-generates variants with batch jobs, so processing isn’t done on-demand. Lambda@Edge routing based on headers can keep costs lower than real-time resizing every request.
C/D? C feels expensive since it processes on every request but caches aggressively, while D stores processed images back to S3, saving reprocessing costs later. D seems more cost-effective long term.
Is it clear if the app should process photos once and cache, or optimize on every request?