Quick Answer
The answer is that the node pool has reached its maximum node count limit. This is the most likely cause because the GKE cluster autoscaler is explicitly constrained by the per-node-pool maximum node count setting; once that ceiling is hit, the autoscaler cannot provision additional nodes regardless of pending pod resource demands, leaving pods stuck in a 'Pending' state due to insufficient CPU. On the Google Professional Cloud Architect exam, this scenario tests your understanding that cluster autoscaler behavior is governed by node pool boundaries, not just cluster-level resource availability—a common trap is assuming the autoscaler will always scale up if resources are needed, overlooking the hard cap configured in the node pool. A useful memory tip: think of the maximum node count as a "glass ceiling" for each pool; once you hit it, the autoscaler stops, so always verify your pool limits when diagnosing scale-up failures.
This PCA practice question tests your understanding of analyze and optimize technical and business processes. 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.
An organization runs a Kubernetes cluster on GKE with cluster autoscaling enabled. They notice that pods are frequently in 'Pending' state due to insufficient CPU, but the cluster autoscaler does not add nodes quickly enough. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
The cluster autoscaler is using the 'least-waste' expander.
Why wrong: That expander is fine.
The horizontal pod autoscaler (HPA) is misconfigured.
Why wrong: HPA scales pods, not nodes.
The pod disruption budget (PDB) is too restrictive.
Why wrong: PDB prevents voluntary disruptions, not scaling.
The node pool has reached the maximum node count limit.
Cluster autoscaler cannot exceed max node limit.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The node pool has reached the maximum node count limit.
Option D is correct because the cluster autoscaler cannot add new nodes if the node pool has already reached its maximum node count limit. This limit is configured at the node pool level in GKE, and once reached, the autoscaler will not scale up further, leaving pods in 'Pending' state due to insufficient CPU resources.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
The cluster autoscaler is using the 'least-waste' expander.
Why it's wrong here
That expander is fine.
The horizontal pod autoscaler (HPA) is misconfigured.
Why it's wrong here
HPA scales pods, not nodes.
The pod disruption budget (PDB) is too restrictive.
Why it's wrong here
PDB prevents voluntary disruptions, not scaling.
The node pool has reached the maximum node count limit.
Why this is correct
Cluster autoscaler cannot exceed max node limit.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the distinction between pod-level scaling (HPA) and node-level scaling (cluster autoscaler), and the trap here is that candidates confuse a restrictive PDB with a node pool limit, or assume the expander strategy directly causes scaling delays.
Detailed technical explanation
The cluster autoscaler in GKE works by monitoring pending pods and requesting additional nodes from the node pool's managed instance group. The maximum node count is a hard limit set in the node pool configuration; when reached, the autoscaler's scale-up logic is blocked, and it logs events like 'no node pool can accommodate the pending pods'. This is distinct from the 'max pods per node' limit, which can also cause pending pods but is not related to node pool size limits.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Analyze and optimize technical and business processes — This question tests Analyze and optimize technical and business processes — Read the scenario before looking for a memorised answer..
The correct answer is: The node pool has reached the maximum node count limit. — Option D is correct because the cluster autoscaler cannot add new nodes if the node pool has already reached its maximum node count limit. This limit is configured at the node pool level in GKE, and once reached, the autoscaler will not scale up further, leaving pods in 'Pending' state due to insufficient CPU resources.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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