An ML engineer must ensure all data in transit is encrypted for an Amazon SageMaker training job, including the communications that SageMaker uses during training. Which action will satisfy this requirement?

Encrypting communication between nodes in a training cluster directly addresses the requirement for encrypting all data in transit, including communications SageMaker uses during training. This ensures that data exchanged within the distributed training environment is secured. Specifying an AWS KMS key when creating the training job request primarily encrypts data at rest (e.g., S3 buckets for input/output), not necessarily data in transit between training nodes. Specifying a KMS key for the SageMaker domain is for general domain-level encryption and doesn't specifically enforce in-transit encryption for individual training jobs. Encrypting communication between nodes for batch processing is irrelevant as the question specifies a training job, not batch processing.