How should the data scientist accomplish this?

Explanations:

While adding an S3 bucket policy can provide access to the SageMaker notebook, it is not the best practice for securely managing permissions. Policies should be attached to IAM roles rather than directly to resources whenever possible. This approach could lead to potential security risks if the ARN is misconfigured or if the notebook instance needs to be recreated with a different ARN.

Encrypting the objects in the S3 bucket with a KMS key restricts access based on encryption keys but does not inherently provide permissions for the SageMaker notebook to access the bucket. The data scientist would still need appropriate permissions to perform actions on the S3 bucket, which are managed through IAM policies, not just encryption settings.

Attaching the appropriate IAM policy to the IAM role associated with the SageMaker notebook instance is the most secure and effective way to provide access to the S3 bucket. This approach follows AWS best practices by granting the necessary permissions at the role level, which can easily be managed and audited. It ensures that only authorized actions can be performed on the specified S3 bucket.

Using a script to configure the AWS CLI with access keys is not a secure practice. Hardcoding access keys can lead to security vulnerabilities if the instance is compromised or if the keys are inadvertently exposed. AWS recommends using IAM roles to grant permissions to instances instead of using access keys.