AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A company requires a resilient architecture that handles asynchronous processing of video transcoding jobs. Their solution should be able to process jobs when the workload spikes without provisioning excess compute capacity. The video transcoding application receives job requests that vary in size and complexity and may take a long time to process. Which of the following solutions is the BEST approach to meet these requirements?
Use AWS Lambda with an Amazon S3 trigger for direct processing as jobs are uploaded to the bucket.
Deploy a single Amazon EC2 instance with an attached Amazon Elastic Block Store (EBS) for job queues and processing.
Store video jobs directly in Amazon S3 and run a cron job on an EC2 instance for periodic processing.
Implement an Amazon SQS queue to buffer job requests and an Auto Scaling group of EC2 instances to process the jobs.
Utilizing Amazon SQS to decouple the job submission from the processing allows the company to handle workload spikes without the need for immediate processing, as jobs can be stored in the queue. The auto-scaling group can then process jobs from the queue based on demand, scaling out to handle more jobs as needed and scaling in when demand decreases. This approach offers a resilient distributed system that can handle variable loads effectively. Implementing a straight serverless approach using AWS Lambda might face limitations due to the execution duration limit, which could be inadequate for long-running transcoding tasks. On the other hand, a single EC2 instance would not provide the necessary scalability and could become a bottleneck during spikes. Lastly, Amazon S3 is not a processing service but rather object storage, so using it alone would not fulfill the transcoding requirements.
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