- A
Use a single larger node (n2-standard-8) to reduce node count and network overhead.
Why wrong: This creates a single point of failure and may not handle spikes efficiently.
- B
Switch to 3 n2-standard-2 nodes to reduce vCPU and memory, and rely on horizontal pod autoscaling.
Why wrong: This may cause resource contention during spikes as the smaller nodes have less capacity.
- C
Create a node pool with smaller nodes (e2-standard-2) using preemptible VMs and enable cluster autoscaler.
Preemptible VMs are cheaper, and autoscaler adds nodes during spikes, maintaining performance.
- D
Keep current node size but use committed use discounts for 1 year to reduce per-hour cost.
Why wrong: Committed use discounts require upfront commitment and may not be optimal if right-sizing is possible.
Quick Answer
The answer is to create a node pool with smaller e2-standard-2 nodes using preemptible VMs and enable the cluster autoscaler. This is correct because preemptible VMs offer a substantial discount—up to 60-91%—over regular instances, making them ideal for reducing baseline costs, while the cluster autoscaler dynamically adds nodes during traffic spikes to maintain performance for latency-sensitive workloads. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of GKE cost optimization strategies, specifically how to balance preemptible VMs with scaling policies; a common trap is choosing larger static nodes, which wastes resources during low traffic. Memory tip: think “small and cheap for the base, autoscale for the burst.”
PCDOE Managing Google Cloud costs Practice Question
This PCDOE practice question tests your understanding of managing google cloud costs. 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 company runs a critical application on Google Kubernetes Engine (GKE) with 3 nodes (e2-standard-4). To reduce costs, the team is considering right-sizing the nodes. The application is latency-sensitive and experiences periodic traffic spikes. What is the most cost-effective approach that maintains performance during spikes?
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
Create a node pool with smaller nodes (e2-standard-2) using preemptible VMs and enable cluster autoscaler.
Option C is the most cost-effective approach because it uses smaller e2-standard-2 nodes with preemptible VMs, which are significantly cheaper than regular VMs, and combines this with the cluster autoscaler to automatically add nodes during traffic spikes. This ensures that the latency-sensitive application maintains performance by scaling out horizontally when needed, while minimizing baseline costs with smaller, cheaper nodes.
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 a single larger node (n2-standard-8) to reduce node count and network overhead.
Why it's wrong here
This creates a single point of failure and may not handle spikes efficiently.
- ✗
Switch to 3 n2-standard-2 nodes to reduce vCPU and memory, and rely on horizontal pod autoscaling.
Why it's wrong here
This may cause resource contention during spikes as the smaller nodes have less capacity.
- ✓
Create a node pool with smaller nodes (e2-standard-2) using preemptible VMs and enable cluster autoscaler.
Why this is correct
Preemptible VMs are cheaper, and autoscaler adds nodes during spikes, maintaining performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keep current node size but use committed use discounts for 1 year to reduce per-hour cost.
Why it's wrong here
Committed use discounts require upfront commitment and may not be optimal if right-sizing is possible.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume preemptible VMs are unsuitable for production or latency-sensitive workloads, but the cluster autoscaler mitigates the risk of VM termination by quickly replacing nodes, making this a valid cost-saving strategy for spike-tolerant applications.
Detailed technical explanation
How to think about this question
Preemptible VMs in GKE are Compute Engine instances that last up to 24 hours and cost about 60-80% less than regular VMs, but they can be terminated at any time. The cluster autoscaler works by monitoring pending pods and automatically adding or removing nodes from a node pool, ensuring that the application can scale out during traffic spikes without manual intervention. In a latency-sensitive application, using a node pool with preemptible VMs combined with the cluster autoscaler allows the cluster to dynamically adjust capacity, while the smaller baseline nodes reduce costs during normal operation.
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.
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FAQ
Questions learners often ask
What does this PCDOE question test?
Managing Google Cloud costs — This question tests Managing Google Cloud costs — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a node pool with smaller nodes (e2-standard-2) using preemptible VMs and enable cluster autoscaler. — Option C is the most cost-effective approach because it uses smaller e2-standard-2 nodes with preemptible VMs, which are significantly cheaper than regular VMs, and combines this with the cluster autoscaler to automatically add nodes during traffic spikes. This ensures that the latency-sensitive application maintains performance by scaling out horizontally when needed, while minimizing baseline costs with smaller, cheaper nodes.
What should I do if I get this PCDOE 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on PCDOE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company runs a web application on Google Kubernetes Engine (GKE) with multiple services. They want to reduce costs without impacting performance. Which THREE actions should they take? (Choose three.)
hard- A.Enable cluster autoscaling and manually scale nodes based on peak load.
- B.Deploy a service mesh like Istio to optimize traffic routing.
- ✓ C.Enable node auto-provisioning to automatically adjust node pools.
- ✓ D.Right-size CPU and memory requests and limits for each service.
- ✓ E.Use preemptible VMs for stateless, fault-tolerant workloads.
Why C: Option C is correct because node auto-provisioning in GKE automatically creates and scales node pools based on the resource requirements of pending pods. This eliminates the need for manual node pool management and ensures that only the necessary compute resources are provisioned, reducing costs without manual intervention or over-provisioning.
Variation 2. A company is using Google Kubernetes Engine (GKE) with multiple node pools. They notice that their monthly costs are higher than expected. Upon review, they find that several preemptible VMs are being recreated frequently, leading to sustained usage costs. What is the most cost-effective solution to reduce costs?
medium- A.Purchase committed use discounts for the preemptible VMs.
- B.Increase the number of preemptible VMs to spread the workload.
- C.Enable sustained use discounts for the existing VMs.
- ✓ D.Migrate to Spot VMs, which have a lower price and no maximum runtime.
Why D: Spot VMs are the recommended replacement for preemptible VMs, offering the same low price but without the 24-hour maximum runtime limit. This eliminates the frequent recreation and sustained usage costs caused by preemptible VMs being terminated and restarted, directly reducing monthly expenses.
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Last reviewed: Jun 11, 2026
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