auto-provisioning = Attaches and deletes node pools to cluster based on the requirements. Hence creating a GPU node pool, and auto-scaling would be betterhttps://cloud.google.com/kubernetes-engine/docs/how-to
/node-auto-provisioning

Google Cloud provides Cloud Audit Logs, which is an integral part of Cloud Logging. It consists of two log streams for each project: Admin Activity and Data Access, which are generated by Google Cloud services to help you answer the question of who did what, where, and when? within your Google Cloud projects.
Ref: https://cloud.google.com/iam/docs/job-functions/auditing#scenario_external_auditors

The correct answer is D because Kubernetes provides a direct rollback command for failed or problematic rollouts. In GKE, deployments are managed using standard Kubernetes deployment mechanics, so kubectl rollout undo is the correct command to return a workload to the previous rollout revision. Kubernetes documentation describes kubectl rollout undo as the command used to roll back to a previous rollout and gives deployment rollback examples. Manually scaling old and new Pods is risky because it bypasses deployment controller state and can create configuration drift. Deleting the deployment causes service disruption and removes the workload object. kubectl rollout restart restarts Pods using the current deployment specification; it does not revert to a previous revision.

https://cloud.google.com/sdk/gcloud/reference/compute/networks/subnets/expand-ip-range gcloud compute networks subnets expand-ip-range - expand the IP range of a Compute Engine subnetwork gcloud compute networks subnets expand-ip-range NAME --prefix-length=PREFIX_LENGTH [--region=REGION] [GCLOUD_WIDE_FLAG ...]