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Difficulty: Very hardDeploying and Managing Google Kubernetes Engine (GKE) Clusters and Workloads

An enterprise telemetry engineering team is designing a scalable batch data processing pipeline on Google Cloud. The workload requires custom Linux kernel parameters (`sysctl` settings) on the underlying cluster nodes to handle high network socket churn. The batch processing jobs are fully fault-tolerant and can recover gracefully from node terminations. Additionally, a cloud engineer needs to configure a new administrator workstation to manage workloads on this newly provisioned cluster. Which deployment and configuration strategy satisfies all requirements while following Google Cloud best practices?

  1. A
    Deploy a GKE Autopilot cluster configured with Spot Pods, and execute `kubectl apply -f deployment.yaml` directly from the administrator workstation.
  2. B
    Deploy a GKE Standard cluster with a Spot node pool using custom node configuration flags, set the default cluster property using `gcloud config set container/cluster <cluster-name>`, and execute `kubectl apply -f deployment.yaml`.
  3. Deploy a GKE Standard cluster containing a Spot VM node pool configured with custom `sysctl` node settings, fetch cluster credentials using `gcloud container clusters get-credentials <cluster-name>`, and execute `kubectl apply -f deployment.yaml`.Answer
  4. D
    Deploy a GKE Standard cluster with a standard, non-preemptible node pool configured with custom sysctl settings, run `kubectl config set-cluster` to establish cluster connectivity, and execute `kubectl apply -f deployment.yaml`.

Answer

Deploy a GKE Standard cluster containing a Spot VM node pool configured with custom `sysctl` node settings, fetch cluster credentials using `gcloud container clusters get-credentials <cluster-name>`, and execute `kubectl apply -f deployment.yaml`.
The correct strategy leverages GKE Standard to allow custom node kernel modifications (`sysctl`), uses Spot VM node pools to minimize compute cost for fault-tolerant batch jobs, and uses `gcloud container clusters get-credentials` to generate the necessary `kubeconfig` credentials for workstation `kubectl` access.

Step-by-Step Solution

1
Evaluate operational mode requirements (Autopilot vs. Standard)
GKE Standard must be selected because GKE Autopilot restricts node-level administration, preventing custom Linux kernel `sysctl` parameters.
Autopilot manages the node infrastructure and locks down system kernel modifications for security and reliability.
2
Determine appropriate node pool compute pricing model
Select a Spot VM node pool for the GKE Standard cluster.
Batch processing jobs are fault-tolerant, making them ideal candidates for Spot VMs to reduce compute expenses by up to 60-91%.
3
Configure local administration workstation authentication
Execute `gcloud container clusters get-credentials <cluster-name> --zone <zone>`.
This command fetches cluster API endpoints and OAuth authentication tokens, updating `$HOME/.kube/config` so `kubectl` can authenticate against the control plane.
4
Deploy workload to the cluster
Execute `kubectl apply -f deployment.yaml` successfully.
With valid `kubeconfig` context pointing to a cluster that supports custom node kernel settings, the deployment manifest applies cleanly.

Key Concept

GKE Cluster Mode Selection, Spot Node Pool Usage, and Kubeconfig Context Generation
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Topics

Deploying and Managing Google Kubernetes Engine (GKE) Clusters and Workloads