Question
A company has a web application that allows users to upload short videos. The videos are stored on Amazon EBS volumes and analyzed by custom recognition software for categorization.
The website contains static content that has variable traffic with peaks in certain months. The architecture consists of Amazon EC2 instances running in an Auto Scaling group for the web application and EC2 instances running in an Auto Scaling group to process an Amazon SQS queue. The company wants to re-architect the application to reduce operational overhead using AWS managed services where possible and remove dependencies on third-party software.
Which solution meets these requirements?
- A. Use Amazon ECS containers for the web application and Spot instances for the Auto Scaling group that processes the SQS queue. Replace the custom software with Amazon Rekognition to categorize the videos.
- B. Store the uploaded videos in Amazon EFS and mount the file system to the EC2 instances for the web application. Process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API to categorize the videos.
- C. Host the web application in Amazon S3. Store the uploaded videos in Amazon S3. Use S3 event notification to publish events to the SQS queue. Process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API to categorize the videos. Correct
- D. Use AWS Elastic Beanstalk to launch EC2 instances in an Auto Scaling group for the web application and launch a worker environment to process the SQS queue. Replace the custom software with Amazon Rekognition to categorize the videos.
Explanation
Option C is correct because it leverages AWS managed services such as Amazon S3 for hosting static content and storing uploaded videos, S3 event notifications to trigger processing via SQS, and AWS Lambda functions to process video categorization using Amazon Rekognition. This approach minimizes operational overhead by reducing dependencies on EC2 instances and third-party software.
