A Company Has a High Performance Computing (HPC) Cluster in Its

A company has a High Performance Computing (HPC) cluster in its on-premises data center, which runs thousands of jobs in parallel for one week every month, processing petabytes of images. The images are stored on a network file server, which is replicated to a disaster recovery site. The on-premises data center has reached capacity and has started to spread the jobs out over the course of the month in order to better utilize the cluster, causing a delay in the job completion. The company has asked its Solutions Architect to design a cost-effective solution on AWS to scale beyond the current capacity of 5,000 cores and 10 petabytes of data. The solution must require the least amount of management overhead and maintain the current level of durability. Which solution will meet the company's requirements?

A) Create a container in the Amazon Elastic Container Registry with the executable file for the job. Use Amazon ECS with Spot Fleet in Auto Scaling groups. Store the raw data in Amazon EBS SC1 volumes and write the output to Amazon S3.
B) Create an Amazon EMR cluster with a combination of On Demand and Reserved Instance Task Nodes that will use Spark to pull data from Amazon S3. Use Amazon DynamoDB to maintain a list of jobs that need to be processed by the Amazon EMR cluster.
C) Store the raw data in Amazon S3, and use AWS Batch with Managed Compute Environments to create Spot Fleets. Submit jobs to AWS Batch Job Queues to pull down objects from Amazon S3 onto Amazon EBS volumes for temporary storage to be processed, and then write the results back to Amazon S3.
D) Submit the list of jobs to be processed to an Amazon SQS to queue the jobs that need to be processed. Create a diversified cluster of Amazon EC2 worker instances using Spot Fleet that will automatically scale based on the queue depth. Use Amazon EFS to store all the data sharing it across all instances in the cluster.