AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A company developing a mobile application wishes to process image uploads by users to provide real-time feedback on image quality. The application experiences unpredictable spikes in use, typically during events and social campaigns, leading to highly variable demand on the backend image-processing system. Which AWS service should be used to handle the image processing in the most cost-effective and scalable way?
Amazon EC2 Auto Scaling group to scale image-processing servers based on demand
AWS Batch to manage image-processing jobs in the cloud
AWS Lambda to run image-processing functions triggered by upload events
AWS Fargate to run the containerized image-processing application without managing servers
AWS Lambda is a serverless compute service that automatically scales per request and charges only for the requests and compute time consumed, allowing it to scale to zero when idle . This makes it ideal for highly unpredictable traffic such as spike-driven image processing.
Amazon EC2 Auto Scaling can add or remove instances automatically, but you still pay for any instances that remain running and you must manage AMIs, patching, and scaling policies. It cannot scale to zero without additional orchestration .
AWS Fargate removes server management but tasks must still be provisioned (for example, via Amazon ECS Service Auto Scaling), and task start-up latency (cold starts of 35 s-2 min) can delay real-time feedback .
AWS Batch is optimized for asynchronous, large-scale batch jobs rather than real-time event-driven workloads .
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