A platform engineering team needs to deploy a high-throughput, fault-tolerant batch processing application to Google Kubernetes Engine (GKE). The application requires custom sysctl kernel parameter tuning and must run on cost-optimized compute resources. Which TWO configuration choices and administrative steps must the team implement to successfully deploy and manage this workload?
- Provision a GKE Standard cluster node pool utilizing Spot VMs and configure the custom sysctl parameters within the pod or node configuration.Cevap
- Run 'gcloud container clusters get-credentials' to update the local kubeconfig file with endpoint and authentication context before executing kubectl deployment commands.Cevap
- CDeploy the application to a GKE Autopilot cluster to automatically enable custom sysctl kernel parameter modifications without managing nodes.
- DConfigure cluster management context for kubectl by executing 'gcloud config set container/cluster' without fetching explicit cluster credentials.
Cevap
The correct requirements are provisioning a GKE Standard cluster using Spot VMs for custom sysctl support, and running 'gcloud container clusters get-credentials' to configure local kubeconfig authentication.
GKE Standard cluster node pools provide full control over node configurations, allowing custom sysctl kernel settings as well as Spot VM provisioning for cost efficiency on stateless batch workloads. To manage workloads via kubectl, executing 'gcloud container clusters get-credentials' fetches cluster endpoint details and updates the local kubeconfig context required for control plane API authorization.
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Anahtar Kavram
GKE Standard operational flexibility and kubeconfig authentication workflows.