A renewable energy company is planning a Google Kubernetes Engine (GKE) cluster architecture to support two distinct cloud workloads:
1. A stateless telemetry ingestion API service with fluctuating traffic, where the operations team requires zero node management overhead and pod-level resource billing.
2. A fault-tolerant, stateless batch calculation engine that processes sensor data off-peak, where reducing compute cost is the primary constraint.
Which TWO cluster configuration decisions should the cloud engineering team implement? (Select TWO.)
- Provision a GKE Autopilot cluster for the telemetry ingestion API workload.Answer
- Create a Spot VM node pool for the off-peak batch calculation engine workload.Answer
- CDeploy a GKE Standard cluster using manually sized static node pools for the telemetry ingestion API workload.
- DConfigure standard On-Demand Compute Engine node pools with persistent storage for the batch calculation engine workload to prevent preemption.
- EConfigure the Horizontal Pod Autoscaler (HPA) to scale the node count of the cluster when batch processing queue depth increases.
Answer
The team should provision a GKE Autopilot cluster for the telemetry ingestion API service and create a Spot VM node pool for the off-peak batch calculation engine workload.
Provisioning a GKE Autopilot cluster satisfies the requirement for zero node management overhead and pod-based billing. Utilizing a Spot VM node pool for the stateless, fault-tolerant batch calculation engine drastically reduces operational compute costs.
Step-by-Step Solution
Key Concept
Planning GKE cluster operational models (Autopilot vs. Standard) and node pool provisioning strategies (Spot VMs)