- A
The model is not deployed correctly.
Why wrong: Model deployment issues would cause prediction errors, not scaling failures.
- B
The endpoint is configured with the wrong machine type.
Why wrong: Machine type does not prevent scaling up.
- C
The CPU utilization is below the target threshold, so the autoscaler does not add replicas.
If CPU utilization is below 60%, the autoscaler sees no need to scale up.
- D
The endpoint is using GPU which cannot autoscale.
Why wrong: GPU endpoints can autoscale based on GPU utilization.
Question 310 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 engineer deploys a model to a Vertex AI endpoint with minReplicas=1 and maxReplicas=3. The endpoint receives a sudden traffic spike, but it does not scale up beyond 1 replica. The CPU utilization target is 60%. 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.
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
✓
The CPU utilization is below the target threshold, so the autoscaler does not add replicas.
Option C is correct because Vertex AI's autoscaler uses CPU utilization as a metric to decide when to add replicas. If the CPU utilization remains below the 60% target threshold, the autoscaler will not trigger scale-up, even during a traffic spike. The endpoint is configured with minReplicas=1 and maxReplicas=3, but without exceeding the target, it stays at the minimum.
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.
- ✗
The model is not deployed correctly.
Why it's wrong here
Model deployment issues would cause prediction errors, not scaling failures.
- ✗
The endpoint is configured with the wrong machine type.
Why it's wrong here
Machine type does not prevent scaling up.
- ✓
The CPU utilization is below the target threshold, so the autoscaler does not add replicas.
Why this is correct
If CPU utilization is below 60%, the autoscaler sees no need to scale up.
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 endpoint is using GPU which cannot autoscale.
Why it's wrong here
GPU endpoints can autoscale based on GPU utilization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume any traffic spike automatically triggers scaling, but Vertex AI's autoscaler only scales based on the configured metric (CPU utilization), not request volume directly.
Detailed technical explanation
How to think about this question
Vertex AI autoscaling uses the Horizontal Pod Autoscaler (HPA) under the hood, which periodically checks the average CPU utilization across all current replicas against the target (60%). If the traffic spike does not increase CPU usage above 60% (e.g., due to I/O-bound or memory-bound workloads), the autoscaler will not add replicas. In real-world scenarios, this often happens with models that are lightweight or optimized, where CPU remains low despite high request rates, leading to under-provisioning.
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.
- →
Serving and Scaling Models — study guide chapter
Learn the concepts, then practise the questions
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Serving and Scaling Models practice questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The CPU utilization is below the target threshold, so the autoscaler does not add replicas. — Option C is correct because Vertex AI's autoscaler uses CPU utilization as a metric to decide when to add replicas. If the CPU utilization remains below the 60% target threshold, the autoscaler will not trigger scale-up, even during a traffic spike. The endpoint is configured with minReplicas=1 and maxReplicas=3, but without exceeding the target, it stays at the minimum.
What should I do if I get this PMLE 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.
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Last reviewed: Jul 4, 2026
This PMLE 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 PMLE exam.
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