A company runs its containerized batch jobs on Amazon ECS. The jobs are scheduled by submitting a container image, a task definition, and the relevant data to an Amazon S3 bucket. Container images may be unique per job. Running the jobs as quickly as possible is of utmost importance, so submitting job artifacts to the S3 bucket triggers the job to run immediately. Sometimes there may be no jobs running at all. However, jobs of any size can be submitted with no prior warning to the IT Operations team. Job definitions include CPU and memory resource requirements. What solution will allow the batch jobs to complete as quickly as possible after being scheduled?

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A. Schedule the jobs on an Amazon ECS cluster using the Amazon EC2 launch type. Use Service Auto Scaling to increase or decrease the number of running tasks to suit the number of running jobs.
B. Schedule the jobs directly on EC2 instances. Use Reserved Instances for the baseline minimum load, and use On-Demand Instances in an Auto Scaling group to scale up the platform based on demand.
C. Schedule the jobs on an Amazon ECS cluster using the Fargate launch type. Use Service Auto Scaling to increase or decrease the number of running tasks to suit the number of running jobs. Most Voted
D. Schedule the jobs on an Amazon ECS cluster using the Fargate launch type. Use Spot Instances in an Auto Scaling group to scale the platform based on demand. Use Service Auto Scaling to increase or decrease the number of running tasks to suit the number of running jobs.

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