How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
׀¡onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook’s KMS role.
Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
Explanations:
Security groups control network traffic but do not grant access to encrypted data in S3 or permissions for KMS decryption. Therefore, they are not relevant here.
While access to the VPC might be useful in some cases, it does not address the necessary permissions for decrypting data from S3 using the correct KMS key.
The IAM role assigned to the SageMaker notebook needs S3 read access and permission to use the KMS key to decrypt the dataset. This is the correct approach.
Assigning the same KMS key to the notebook instance does not automatically grant access to the KMS-encrypted data in S3. Permissions are required in the KMS key policy.
I reckon the answer is:
Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.