- A
Increase the HPA target CPU utilization to 90% to reduce the number of replicas needed.
Why wrong: Raising the target CPU utilization would make pods busier, potentially causing performance degradation; it does not solve the node capacity limit.
- B
Reduce the pod resource requests for CPU so that more pods can fit on existing nodes.
Why wrong: Lowering resource requests could allow more pods per node, but may cause CPU contention and performance issues; it is a temporary workaround, not a proper scaling solution.
- C
Increase the maximum number of nodes in the node pool to allow more capacity.
The node pool has a max of 10 nodes; increasing this limit allows the Cluster Autoscaler to provision additional nodes, resolving the pending pods.
- D
Enable extra capacity by creating a second node pool with preemptible VMs.
Why wrong: While this adds capacity, preemptible VMs can be reclaimed at any time, risking pod disruptions; the immediate root cause is the node pool max limit, which is simpler to fix.
Cloud Digital Leader Scaling with Google Cloud operations Practice Question
This GCDL practice question tests your understanding of scaling with google cloud operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 an e-commerce platform on Google Kubernetes Engine (GKE) using autoscaling. They have a baseline workload and occasional traffic spikes during promotions. They configured a Horizontal Pod Autoscaler (HPA) for their web application pods and a Cluster Autoscaler for the node pool. The HPA targets 70% CPU utilization. During a recent sales event, traffic exceeded expectations. The operations team observed that the HPA increased the desired number of replicas to 50, but only 20 pods were running. The remaining 30 pods were in 'Pending' status. The Cluster Autoscaler logs show repeated messages: 'no capacity to scale up node pool'. The node pool is configured with a maximum of 10 nodes, each with 4 vCPUs, and currently 8 nodes are running. The team checked the node pool's current utilization and found that nodes are near capacity. What should the team do to ensure the application scales correctly during future events?
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
Increase the maximum number of nodes in the node pool to allow more capacity.
The HPA requested 50 replicas, but only 20 could be scheduled because the existing 8 nodes (each with 4 vCPUs) are near capacity. The Cluster Autoscaler cannot add more nodes because the node pool is capped at 10 nodes. Increasing the maximum number of nodes in the node pool (Option C) allows the Cluster Autoscaler to provision additional nodes to accommodate the pending pods, enabling the HPA to scale as needed.
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.
- ✗
Increase the HPA target CPU utilization to 90% to reduce the number of replicas needed.
Why it's wrong here
Raising the target CPU utilization would make pods busier, potentially causing performance degradation; it does not solve the node capacity limit.
- ✗
Reduce the pod resource requests for CPU so that more pods can fit on existing nodes.
Why it's wrong here
Lowering resource requests could allow more pods per node, but may cause CPU contention and performance issues; it is a temporary workaround, not a proper scaling solution.
- ✓
Increase the maximum number of nodes in the node pool to allow more capacity.
Why this is correct
The node pool has a max of 10 nodes; increasing this limit allows the Cluster Autoscaler to provision additional nodes, resolving the pending pods.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable extra capacity by creating a second node pool with preemptible VMs.
Why it's wrong here
While this adds capacity, preemptible VMs can be reclaimed at any time, risking pod disruptions; the immediate root cause is the node pool max limit, which is simpler to fix.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that adjusting HPA thresholds or pod resource requests alone can solve capacity issues, when the real bottleneck is the node pool's maximum node limit, which must be increased to allow the Cluster Autoscaler to add nodes.
Detailed technical explanation
How to think about this question
The Cluster Autoscaler works by monitoring pending pods and attempting to add nodes to the node pool, but it is constrained by the pool's 'maxNodeCount' setting. In GKE, the HPA and Cluster Autoscaler operate independently; the HPA sets the desired replica count, while the Cluster Autoscaler handles node-level scaling. A common subtlety is that if the node pool's maximum is reached, the Cluster Autoscaler logs 'no capacity to scale up' and stops, leaving pods pending indefinitely. Real-world scenarios often require tuning both HPA thresholds and node pool limits to handle burst traffic.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this GCDL question test?
Scaling with Google Cloud operations — This question tests Scaling with Google Cloud operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the maximum number of nodes in the node pool to allow more capacity. — The HPA requested 50 replicas, but only 20 could be scheduled because the existing 8 nodes (each with 4 vCPUs) are near capacity. The Cluster Autoscaler cannot add more nodes because the node pool is capped at 10 nodes. Increasing the maximum number of nodes in the node pool (Option C) allows the Cluster Autoscaler to provision additional nodes to accommodate the pending pods, enabling the HPA to scale as needed.
What should I do if I get this GCDL 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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Last reviewed: Jun 30, 2026
This GCDL 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 GCDL exam.
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