You have a 2 TB dataset in Amazon S3 and a trained model container stored in Amazon ECR. You must run a cost-effective, scalable batch scoring job for the dataset. Which option is the most cost-effective scalable solution?

The correct option leverages AWS Batch with managed Amazon EC2 Spot Instances for cost-effectiveness and scalability. Spot Instances offer significant savings (up to 90%) compared to On-Demand instances, making them ideal for fault-tolerant batch processing. AWS Batch manages the provisioning and scaling of these instances. Amazon FSx for Lustre provides a high-performance, scalable file system optimized for compute-intensive workloads, ensuring fast access to the 2 TB dataset. Incorrect options: Using EC2 Reserved Instances with AWS Batch is less cost-effective than Spot Instances for a batch job where interruptions are tolerable. EC2 instance-store volumes are ephemeral and not suitable for persistent data shared across a cluster. Running from a SageMaker notebook on EC2 Reserved Instances is not designed for large-scale batch scoring; notebooks are interactive development environments. EBS volumes are suitable for single instances but less performant for distributed, high-throughput access than FSx for Lustre. Running from a SageMaker notebook on EC2 Spot Instances with Amazon EFS is still not the best solution for large-scale batch scoring. EFS is a general-purpose file system, but FSx for Lustre offers superior performance for high-throughput, parallel workloads, and notebooks are not the appropriate execution environment for this scale.