AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A startup is building a lightweight IoT backend that receives intermittent and unpredictable messages from devices in the field. The backend needs to process messages concurrently and scale precisely with incoming traffic to optimize costs. Management prefers a solution that minimizes operational overhead. Which of the following would provide the optimal balance of cost-efficiency and scalability?
Use AWS Batch to manage message processing jobs, relying on its queuing mechanism to handle bursts in traffic.
Provision Amazon EC2 Spot Instances to handle the message load and reduce costs by bidding on spare computing capacity.
Deploy the backend using Amazon ECS on AWS Fargate to smoothly scale with workload changes, minimizing server management.
Implement the backend with AWS Lambda to process messages with automatic scaling and no infrastructure management.
AWS Lambda is most suitable for the described scenario as it provides a serverless environment that can handle sporadic, concurrent requests without the need for server management. Lambda allows the system to scale automatically and precisely with the incoming workload, ensuring that costs directly correlate to usage. While AWS Batch efficiently processes batch jobs, its predetermined scaling may not align perfectly with unpredictable message arrival times, leading to possible delays or over-provisioning. Amazon EC2 Spot Instances are cost-effective but require management for in-depth capacity and scaling, which does not match the management's preference. Amazon ECS on AWS Fargate could meet the needs but will incur more costs compared to Lambda because Fargate’s pricing is based on container runtime and resources regardless of message arrivals.
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