All the listed steps are essential for a robust CIICD pipeline. Building the container image encapsulates your application, pushing it to a registry ensures easy access, applying Kubernetes configurations defines the deployment, and automated tests validate the application's functionality before deployment.

The query •process_cpu_seconds_total{pod="", container="", process=""Y is designed to retrieve CPU usage metrics for specific processes running within a container. The other options are not focused on individual process metrics.

You need a custom controller to bridge the gap between the HPA and Prometheus. The controller will fetch metrics from Prometheus and provide them to the HPA. You can use the '-metrics' flag in the HPA YAML file to specify the custom metrics and their sources (including the Prometheus endpoint).

ArgoCD can be configured to use webhooks, which are HTTP callbacks triggered by events like image pushes to container registries. This allows ArgoCD to automatically update the cluster whenever a new image tag is pushed, ensuring your deployments are always using the latest image versions.

The correct approach is to create a separate PersistentVolumeClaim (PVC) that specifies the desired storage class and reference the PVC in the deployments 'spec_template.spec_containers[0]_volumeMounts[0]_name' field. This ensures the PVC is automatically bound to a PV with the correct storage class. Specifying the storage class name directly within the deployment or the volumeMounts section is not the standard practice for defining storage requirements.