- A
Review billing export data in BigQuery to identify the top cost contributors by project, service, and label.
Why wrong: While useful, this is a manual analysis that may not highlight specific optimization opportunities like GKE recommender.
- B
Use the GKE cost optimization recommender to identify idle resources and apply recommended changes.
The recommender provides specific, actionable insights to reduce costs without impacting workload stability.
- C
Disable node auto-provisioning and switch all nodes to preemptible instances.
Why wrong: Preemptible nodes may be terminated at any time, disrupting stateful workloads.
- D
Create a budget alert for the GKE service and set a hard limit to prevent further overspend.
Why wrong: Budget alerts notify but do not reduce costs; hard limits may cause service disruption.
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 production workload on Google Kubernetes Engine (GKE) with cluster autoscaling enabled. The cluster has three node pools: one for general-purpose workloads (n1-standard-4), one for memory-intensive workloads (n2-highmem-8), and one for GPU-accelerated jobs (with 1 NVIDIA Tesla T4 per node). The workloads are a mix of stateless microservices and stateful databases. Over the past month, the monthly GKE cost has increased by 40% despite no significant change in application traffic or resource requests. The team has verified that vertical pod autoscaling and node auto-provisioning are enabled. They have also checked that there are no orphaned resources. They suspect that overspending is due to inefficient resource utilization or node selection. What should the team do to identify and reduce the unnecessary cost?
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 the GKE cost optimization recommender to identify idle resources and apply recommended changes.
Option B is correct because the GKE cost optimization recommender specifically analyzes cluster utilization patterns, such as underutilized nodes or pods, and provides actionable recommendations to right-size resources. Since the team has already verified that VPA and node auto-provisioning are enabled and no orphaned resources exist, the recommender can pinpoint inefficiencies like over-provisioned node pools or idle GPU nodes that are driving the 40% cost increase despite stable traffic.
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.
- ✗
Review billing export data in BigQuery to identify the top cost contributors by project, service, and label.
Why it's wrong here
While useful, this is a manual analysis that may not highlight specific optimization opportunities like GKE recommender.
- ✓
Use the GKE cost optimization recommender to identify idle resources and apply recommended changes.
Why this is correct
The recommender provides specific, actionable insights to reduce costs without impacting workload stability.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Disable node auto-provisioning and switch all nodes to preemptible instances.
Why it's wrong here
Preemptible nodes may be terminated at any time, disrupting stateful workloads.
- ✗
Create a budget alert for the GKE service and set a hard limit to prevent further overspend.
Why it's wrong here
Budget alerts notify but do not reduce costs; hard limits may cause service disruption.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse high-level billing analysis (Option A) with actionable optimization recommendations, or they assume that disabling auto-provisioning and using preemptible instances (Option C) is a universal cost-saving fix without considering workload compatibility and the need for diagnostic insights first.
Detailed technical explanation
How to think about this question
The GKE cost optimization recommender uses machine learning models trained on historical cluster metrics (CPU, memory, GPU utilization) to detect patterns like over-provisioned node pools, idle nodes, or pods with resource requests far exceeding actual usage. It can recommend specific actions such as reducing node count, switching to cheaper machine series (e.g., from n2-highmem-8 to n2-standard-8 if memory is underutilized), or consolidating workloads onto fewer nodes. In practice, a common subtlety is that GPU nodes are often left idle because GPU-accelerated jobs are sporadic, yet the cluster autoscaler may keep them running due to pod anti-affinity or taint misconfigurations, leading to significant cost without the recommender flagging them.
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.
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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: Use the GKE cost optimization recommender to identify idle resources and apply recommended changes. — Option B is correct because the GKE cost optimization recommender specifically analyzes cluster utilization patterns, such as underutilized nodes or pods, and provides actionable recommendations to right-size resources. Since the team has already verified that VPA and node auto-provisioning are enabled and no orphaned resources exist, the recommender can pinpoint inefficiencies like over-provisioned node pools or idle GPU nodes that are driving the 40% cost increase despite stable traffic.
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
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Last reviewed: Jun 25, 2026
This PCDOE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDOE exam.
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