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 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.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable node auto-provisioning to automatically adjust node pools.
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.
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.
✗
Enable cluster autoscaling and manually scale nodes based on peak load.
Why it's wrong here
Manual scaling is not efficient; node auto-provisioning is better for cost.
✗
Deploy a service mesh like Istio to optimize traffic routing.
Why it's wrong here
Service mesh adds overhead and cost, not reducing costs.
✓
Enable node auto-provisioning to automatically adjust node pools.
Why this is correct
Node auto-provisioning ensures the cluster uses the right size and type of nodes.
Related concept
Read the scenario before looking for a memorised answer.
✓
Right-size CPU and memory requests and limits for each service.
Why this is correct
Right-sizing avoids over-provisioning and reduces node resource waste.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use preemptible VMs for stateless, fault-tolerant workloads.
Why this is correct
Preemptible VMs are cheaper and suitable for fault-tolerant stateless services.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that manual scaling or service meshes are cost-saving measures, when in fact they either increase costs or fail to address the root cause of over-provisioning.
Detailed technical explanation
How to think about this question
Node auto-provisioning leverages the cluster autoscaler to dynamically create new node pools with optimized machine types based on pod resource requests, using the Google Cloud Compute Engine API to provision instances. It considers pod affinity, taints, and tolerations to ensure efficient bin-packing, reducing wasted capacity. In a real-world scenario, a service with varying memory demands would trigger auto-provisioning to spin up a high-memory node pool only when needed, avoiding the cost of a permanently large node pool.
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.
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: Enable node auto-provisioning to automatically adjust node pools. — 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.
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.
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Question Discussion
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