- A
Cluster autoscaler only scales down once per day
Why wrong: Cluster autoscaler can scale down multiple times per day — there's no daily-once restriction.
- B
Pods have a PodDisruptionBudget preventing eviction, or nodes are still within the scale-down cooldown period
Cluster autoscaler delays scale-down to avoid premature removal of needed capacity. PodDisruptionBudgets that prevent Pod eviction also block node removal.
- C
The max node count prevents scale-down — setting max=10 locks the cluster at 10 nodes
Why wrong: The `max` value is the upper limit for scale-out — it doesn't prevent scale-down. Scale-in is governed by min and autoscaler heuristics.
- D
Cloud Monitoring alerts are pausing the autoscaler to investigate the traffic drop
Why wrong: Monitoring alerts don't interact with the cluster autoscaler — they notify teams but don't pause autoscaling.
Quick Answer
The answer is that the cluster autoscaler is likely blocked by a PodDisruptionBudget (PDB) or is still within the scale-down cooldown period. After a traffic surge triggers a scale-up to 10 nodes, the autoscaler enforces a default 10-minute cooldown before it can begin scaling down, but more critically, it will not evict pods that are protected by a PDB—for example, a PDB requiring at least 3 replicas of a critical service. If those pods are spread across the extra nodes, the autoscaler cannot safely terminate them, leaving the cluster stuck at 10 nodes even after traffic drops. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of how the autoscaler interacts with PDBs and cooldown timers; a common trap is assuming the autoscaler will immediately downscale once load decreases, ignoring these safeguards. Remember the memory tip: “PDB or cooldown—the autoscaler won’t cut nodes down.”
Google ACE Practice Question: Ensuring successful operation of a cloud solution
This ACE practice question tests your understanding of ensuring successful operation of a cloud solution. 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 GKE node pool has an autoscaler configured with min=2, max=10 nodes. After a sustained traffic surge, the cluster scaled to 10 nodes. Traffic drops overnight to its normal level, but the cluster remains at 10 nodes after 4 hours. What is the most likely reason?
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.
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
Pods have a PodDisruptionBudget preventing eviction, or nodes are still within the scale-down cooldown period
Option B is correct because the cluster autoscaler will not scale down nodes that are hosting pods protected by a PodDisruptionBudget (PDB) that prevents eviction, or if the nodes are still within the scale-down cooldown period (default 10 minutes after a scale-up). Since the cluster scaled up to 10 nodes during the traffic surge, the autoscaler must wait for the cooldown to expire and for all pods to be safely evictable before removing nodes. If any pods have a PDB that blocks eviction (e.g., requiring at least 3 replicas), the autoscaler cannot terminate the underlying nodes, keeping the cluster at 10 nodes even after traffic drops.
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.
- ✗
Cluster autoscaler only scales down once per day
Why it's wrong here
Cluster autoscaler can scale down multiple times per day — there's no daily-once restriction.
- ✓
Pods have a PodDisruptionBudget preventing eviction, or nodes are still within the scale-down cooldown period
Why this is correct
Cluster autoscaler delays scale-down to avoid premature removal of needed capacity. PodDisruptionBudgets that prevent Pod eviction also block node removal.
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.
- ✗
The max node count prevents scale-down — setting max=10 locks the cluster at 10 nodes
Why it's wrong here
The `max` value is the upper limit for scale-out — it doesn't prevent scale-down. Scale-in is governed by min and autoscaler heuristics.
- ✗
Cloud Monitoring alerts are pausing the autoscaler to investigate the traffic drop
Why it's wrong here
Monitoring alerts don't interact with the cluster autoscaler — they notify teams but don't pause autoscaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that the autoscaler's max node count prevents scale-down, when in fact the max only caps scale-up, and scale-down is blocked by cooldown periods or PodDisruptionBudgets.
Detailed technical explanation
How to think about this question
The GKE cluster autoscaler uses a 10-minute scale-down cooldown (configurable via `--scale-down-delay-after-add`) to prevent flapping after a scale-up event. Additionally, it checks PodDisruptionBudgets (PDBs) before evicting pods; if a PDB specifies `minAvailable` or `maxUnavailable` that would be violated, the node is considered 'unsafe to remove' and is skipped. In real-world scenarios, stateful workloads (e.g., databases with PDBs requiring 3 replicas) often cause nodes to remain active longer than expected after traffic drops, leading to higher costs.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Ensuring successful operation of a cloud solution — study guide chapter
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FAQ
Questions learners often ask
What does this ACE question test?
Ensuring successful operation of a cloud solution — This question tests Ensuring successful operation of a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Pods have a PodDisruptionBudget preventing eviction, or nodes are still within the scale-down cooldown period — Option B is correct because the cluster autoscaler will not scale down nodes that are hosting pods protected by a PodDisruptionBudget (PDB) that prevents eviction, or if the nodes are still within the scale-down cooldown period (default 10 minutes after a scale-up). Since the cluster scaled up to 10 nodes during the traffic surge, the autoscaler must wait for the cooldown to expire and for all pods to be safely evictable before removing nodes. If any pods have a PDB that blocks eviction (e.g., requiring at least 3 replicas), the autoscaler cannot terminate the underlying nodes, keeping the cluster at 10 nodes even after traffic drops.
What should I do if I get this ACE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
Last reviewed: Jun 30, 2026
This ACE 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 ACE exam.
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