The answer is to spread node pools across multiple zones and set appropriate minimum and maximum node counts with Node Auto-Provisioning. This combination directly addresses GKE cost optimization and autoscaling across zones because Node Auto-Provisioning works with the cluster autoscaler to automatically create or delete node pools based on workload demands, while multi-zone distribution ensures high availability by spreading pods across failure domains. On the Google Professional Cloud Developer exam, this tests your understanding of how to balance cost and resilience—a common trap is assuming that simply enabling autoscaling is enough, without realizing that zone-level distribution and scaling limits are required to prevent both single points of failure and unexpected cost spikes. The key insight is that Node Auto-Provisioning can scale down to zero when idle, but only if you explicitly set minimum node counts to zero and maximums to cap spending. Memory tip: think “spread and cap”—spread across zones for availability, cap the node counts for cost control.
PCD Practice Question: Designing highly scalable, available, and reliable cloud-native applications
This PCD practice question tests your understanding of designing highly scalable, available, and reliable cloud-native applications. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team uses Google Kubernetes Engine (GKE) with Node Auto-Provisioning. They want to optimize cost while maintaining high availability across zones. Which two strategies should they implement? (Select exactly 2.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use cluster autoscaler with appropriate min and max node counts
Option A is correct because Node Auto-Provisioning (NAP) in GKE works in conjunction with the cluster autoscaler to automatically create and delete node pools based on workload demands. By setting appropriate minimum and maximum node counts, you ensure the cluster can scale down to zero when idle (saving cost) and scale up to handle peak load, while avoiding runaway scaling that could increase costs unexpectedly.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 cluster autoscaler with appropriate min and max node counts
Why this is correct
The cluster autoscaler automatically adjusts node count based on demand, optimizing cost.
Related concept
Read the scenario before looking for a memorised answer.
✓
Spread node pools across multiple zones
Why this is correct
Distributing nodes across zones protects against zone failures.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use preemptible VMs for all node pools
Why it's wrong here
Preemptible VMs can be terminated at any time, which compromises high availability.
✗
Disable cluster autoscaler to prevent scaling
Why it's wrong here
Disabling the autoscaler removes automatic scaling, which can lead to over-provisioning or under-provisioning.
✗
Use sole-tenant nodes for high availability
Why it's wrong here
Sole-tenant nodes provide physical isolation but do not inherently improve availability or cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse preemptible VMs (cost-saving but low availability) with high availability, or assume that disabling the autoscaler prevents cost spikes, when in fact it leads to either over-provisioning or under-provisioning, both of which harm the dual goal of cost optimization and high availability.
Detailed technical explanation
How to think about this question
Node Auto-Provisioning uses the cluster autoscaler’s expander logic to select the most cost-efficient machine type from a predefined set, based on pending pod resource requests. Under the hood, the autoscaler evaluates unschedulable pods every 10 seconds and triggers node pool creation or deletion via the GKE API, respecting the min/max constraints per node pool. In a real-world scenario, if you set min=1 and max=10 across three zones, the cluster can survive a single zone failure while still scaling down to one node during off-peak hours, optimizing cost without sacrificing resilience.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Designing highly scalable, available, and reliable cloud-native applications — This question tests Designing highly scalable, available, and reliable cloud-native applications — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use cluster autoscaler with appropriate min and max node counts — Option A is correct because Node Auto-Provisioning (NAP) in GKE works in conjunction with the cluster autoscaler to automatically create and delete node pools based on workload demands. By setting appropriate minimum and maximum node counts, you ensure the cluster can scale down to zero when idle (saving cost) and scale up to handle peak load, while avoiding runaway scaling that could increase costs unexpectedly.
What should I do if I get this PCD question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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