An online event-analytics organization is planning a Google Kubernetes Engine (GKE) cluster architecture. The environment will host web ingestion APIs alongside stateless, fault-tolerant background data transformation workloads. The engineering team requires an architecture that minimizes cluster node management overhead while significantly reducing compute costs for the stateless background workloads. Which TWO architectural decisions should you select to fulfill these requirements?
- Provision a GKE Autopilot cluster to eliminate manual node provisioning, scaling, and infrastructure maintenance overhead.Cevap
- Configure Spot Pod specifications for the stateless background processing workloads to run on spare GCP capacity at reduced rates.Cevap
- CDeploy GKE Standard with Spot VM node pools dedicated to non-fault-tolerant stateful database workloads to lower operational costs.
- DConfigure the Horizontal Pod Autoscaler (HPA) to scale the count of underlying Compute Engine VM node instances when workload queue length increases.
- EGenerate long-lived Service Account JSON keys and mount them inside Pods via Kubernetes Secrets to authenticate to Google Cloud APIs.
Cevap
Deploying a GKE Autopilot cluster and configuring Spot Pod specifications for stateless background processing workloads.
GKE Autopilot delegates node and cluster infrastructure management to Google Cloud, satisfying the requirement to minimize operational overhead. Configuring Spot Pods allows fault-tolerant, stateless background workloads to run on discounted, preemptible compute resources, satisfying the requirement to reduce compute costs.
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GKE Autopilot Architecture and Spot Workload Provisioning
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