When using containers to run training jobs, which settings must be specified for Vertex AI to execute your training code?

The correct response encompasses critical elements for executing training jobs in Vertex AI when utilizing containers.

Specifying the region is essential to ensure that the training job runs in a suitable data center that is geographically and regionally relevant to your application's needs. It directly impacts both latency and compliance with data residency regulations.

The display-name serves as an identifier for your training job, making it easier to manage and monitor multiple jobs. Clear naming conventions help distinguish between various training initiatives, especially in environments with numerous parallel tasks.

Worker-pool-spec is crucial as it defines the configuration of resources for the training task, including the machine types, number of machines, and the container image that contains your training code. This allows Vertex AI to allocate the right amount of compute resources according to the job's requirements.

Overall, these elements work together to ensure that your training code runs efficiently and effectively in the Vertex AI environment.