A company stores clickstream data in an S3 bucket in AWS Account A and needs to train an ML model in SageMaker AI in Account B for a 10-day job. Training must use only private IP addresses and no training metadata may be shared with AWS. Which solution meets these constraints?

Creating a VPC endpoint for S3 ensures that data transfer between SageMaker and S3 occurs entirely within the AWS network using private IP addresses, fulfilling the private IP requirement. Setting the SageMaker AI OPTOUTTRACKING environment variable to 1 explicitly disables the collection of training metadata by SageMaker, addressing the second constraint. VPC peering would allow private communication but doesn't directly address metadata collection. Contacting AWS by email for opt-out is not a standard or reliable method for programmatic control. Configuring a security group policy on the S3 bucket is a good security practice but doesn't ensure private IP usage for cross-account S3 access or prevent metadata collection. Generating presigned URLs allows temporary access but doesn't guarantee private IP communication for the entire training job or prevent metadata collection without the environment variable.