Soru

Zorluk: KolayPlanning Google Kubernetes Engine (GKE) Cluster Architectures

A cloud administrator is planning a new Google Kubernetes Engine (GKE) cluster to host stateless web microservices. The primary objective is to minimize node management overhead so that Google fully manages the underlying worker node infrastructure and OS patching, while charging only for the resource requests of deployed Pods. Which TWO architectural choices or operational features should the administrator select to fulfill these requirements?

  1. Deploy the cluster using GKE Autopilot mode.Cevap
  2. Define explicit CPU, memory, and storage resource requests for each Pod specification.Cevap
  3. C
    Deploy the cluster using GKE Standard mode with custom node pools.
  4. D
    Use Spot VMs for all node pools hosting persistent stateful database workloads.
  5. E
    Configure Horizontal Pod Autoscaler (HPA) to automatically provision additional Compute Engine VM nodes.

Cevap

The administrator should deploy the cluster using GKE Autopilot mode and specify explicit CPU, memory, and storage resource requests in the Pod manifests.
GKE Autopilot is Google Cloud's fully managed cluster mode where Google handles all node management, security patching, and infrastructure scaling. In Autopilot, billing is based directly on the CPU, memory, and storage requested in individual Pod manifests, eliminating payment for unused node capacity.

Adım Adım Çözüm

1
Analyze the operational requirements for cluster management overhead.
GKE Autopilot fully automates node lifecycle management, OS patching, and node pool administration.
Choosing Autopilot shifts worker node management responsibility to Google.
2
Determine how billing and resource scheduling function in Autopilot mode.
Pod resource requests (CPU, memory, storage) define both billing boundaries and scheduling requirements.
Autopilot charges per Pod based on specified resource requests rather than paying for idle compute capacity on unmanaged worker nodes.

Anahtar Kavram

GKE Autopilot cluster architecture and Pod-based resource planning
Bu soruyu puanla