How should the Data Science team configure the notebook instance placement to meet these requirements?
Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
Explanations:
The S3 bucket and SageMaker endpoint should be within the same VPC, but this alone doesn’t ensure secure access with private networking. A VPC endpoint for S3 and SageMaker must be used.
Using IAM policies alone does not address the requirement for private network traffic. VPC endpoints for S3 and SageMaker are needed to ensure traffic stays within AWS network.
Associating the notebook with a private subnet and using VPC endpoints for both S3 and SageMaker ensures that all data communication remains within the AWS network without internet access.
A NAT gateway is not required as it provides internet access. The requirement is for no internet access, so this option violates that rule. VPC endpoints are a better choice.
I deduce that the answer is:
Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.