Google ACE Planning and Configuring a Cloud Solution Practice Question
An organization needs to deploy a microservices application on Google Kubernetes Engine. Each microservice has different resource requirements, and the team wants to optimize costs by using a mix of spot (preemptible) and regular nodes. They also need to ensure that critical services run on regular nodes. Which GKE feature allows this separation?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use node pools with taints and tolerations on the pods
Node pools in GKE allow you to have groups of nodes with different configurations (e.g., machine type, preemptible vs on-demand). You can then use node affinity or taints/tolerations to schedule pods onto the appropriate node pool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use resource quotas to limit namespace resource usage
Why it's wrong here
Resource quotas (ResourceQuota) constrain aggregate CPU, memory, and object counts within a namespace; they do not influence pod-to-node scheduling decisions. A quota cannot distinguish a spot node from a standard node, so it cannot prevent critical pods from landing on preemptible infrastructure. To segregate by hardware lifecycle, you need node-level mechanisms, not namespace-level limits.
- ✗
Use separate clusters for critical and non-critical services
Why it's wrong here
Separate clusters for critical and non-critical services isolate control planes and failure domains, but they multiply operational overhead: more clusters mean more control-plane costs, more monitoring, and more complex networking and SRE workflows. In GKE the standard pattern is to use multiple node pools—spot and regular—inside one cluster and separate workloads with taints, tolerations, and node affinity. Cluster separation is a heavier, costlier solution than the targeted node-level approach.
- ✓
Use node pools with taints and tolerations on the pods
Why this is correct
Create two node pools, e.g. a regular pool for critical services and a spot/preemptible pool for non-critical work, then taint the spot pool with a key such as spot=true:NoSchedule. Critical pods are deployed without the matching toleration, so the Kubernetes scheduler will never place them on spot nodes; non-critical pods include the toleration and can use the cheaper spot capacity. This precisely controls placement while keeping a single cluster and simplifying operations.
- ✗
Use vertical pod autoscaling
Why it's wrong here
Vertical Pod Autoscaling (VPA) analyzes historical usage and updates CPU/memory requests (and sometimes limits) to right-size pods; it has no notion of node lifecycle, taints, or hardware pools. Because VPA only changes resource quantities, it cannot steer pods toward or away from spot nodes. In fact, VPA may resize a pod so it still lands on whatever node matches its existing scheduling constraints—it is a sizing tool, not a placement tool.
Go deeper
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Google Cloud Platform Overview
Key term
Anthos
Anthos is a Google Cloud platform that lets you run applications consistently across different computing environments, like on-premises data centers and multiple public clouds.
Key term
Organization
An Organization is a top-level container in Google Cloud that represents your company or entities and serves as the root node for all your cloud resources, policies, and access control.
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