A company runs a Python script on an Amazon EC2 instance to process data. The script runs every 10 minutes. The script ingests files from an Amazon S3 bucket and processes the files. On average, the script takes approximately 5 minutes to process each file The script will not reprocess a file that the script has already processed. The company reviewed Amazon CloudWatch metrics and noticed that the EC2 instance is idle for approximately 40% of the time because of the file processing speed. The company wants to make the workload highly available and scalable. The company also wants to reduce long-term management overhead. Which solution will meet these requirements MOST cost-effectively? A. Migrate the data processing script to an AWS Lambda function. Use an S3 event notification to invoke the Lambda function to process the objects when the company uploads the objects. B. Create an Amazon Simple Queue Service (Amazon SQS) queue. Configure Amazon S3 to send event notifications to the SQS queue. Create an EC2 Auto Scaling group with a minimum size of one instance. Update the data processing script to poll the SQS queue. Process the S3 objects that the SQS message identifies. C. Migrate the data processing script to a container image. Run the data processing container on an EC2 instance. Configure the container to poll the S3 bucket for new objects and to process the resulting objects. D. Migrate the data processing script to a container image that runs on Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Create an AWS Lambda function that calls the Fargate RunTaskAPI operation when the container processes the file. Use an S3 event notification to invoke the Lambda function.