A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket. How should the data scientist accomplish this?

While an S3 bucket policy can grant access, it's generally best practice for AWS services to access S3 using their associated IAM roles, making the IAM role policy the more appropriate and manageable place to define permissions for a SageMaker notebook instance.

Encrypting objects in an S3 bucket with a custom AWS KMS key protects data at rest but does not, by itself, grant the SageMaker notebook instance permission to access (read, write) those objects; access control is handled by IAM policies.

Attaching an IAM policy with the required S3 permissions (GetObject, PutObject, ListBucket) to the IAM role associated with the Amazon SageMaker notebook instance is the standard, secure, and recommended method for granting the instance access to S3 data. This approach leverages AWS IAM for fine-grained authorization without exposing explicit credentials.

Using a lifecycle configuration script to configure the AWS CLI with explicit access keys or even temporary tokens is less secure and less manageable than relying on the inherent IAM role attached to the notebook instance, which automatically provides temporary credentials.