- A
Data skew causing longer inference time
Why wrong: Data skew would affect prediction accuracy more than latency, and it would not explain the doubled concurrency with low CPU.
- B
Memory leak in the serving container
Why wrong: A memory leak would typically cause memory utilization to increase over time and might eventually degrade performance, but CPU remains low and memory is not mentioned as high.
- C
Insufficient number of replicas for autoscaling
If the number of replicas is not scaling fast enough to match increased concurrency, requests queue up, leading to higher latency while each replica's CPU is underutilized.
- D
Model overfitting
Why wrong: Overfitting reduces generalization but does not directly cause increased latency; it may cause incorrect predictions but not slower inference.
Quick Answer
The answer is insufficient replicas for autoscaling. This is correct because when diagnosing prediction latency increase due to insufficient replicas, the key clue is that CPU utilization remains below 40% while concurrent requests have doubled—this indicates the existing replicas are not CPU-bound but are instead bottlenecked by request queuing or thread contention. Vertex AI’s default autoscaling scales on CPU utilization, so if the threshold isn’t crossed, new replicas are never provisioned, causing requests to pile up and latency to spike. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of autoscaling triggers and the common trap of assuming low CPU means no scaling is needed. A frequent mistake is to look for a code or model issue, but the real problem is operational: the scaling policy doesn’t match the workload pattern. Remember the mnemonic: “Low CPU, high queue—check your scaling view.”
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
A machine learning engineer notices that the online prediction latency for a custom TensorFlow model deployed on Vertex AI has increased significantly over the past week. Cloud Monitoring shows that the CPU utilization of the endpoints remains below 40%, but the number of concurrent requests has doubled. What is the most likely cause of the latency increase?
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
Insufficient number of replicas for autoscaling
Option C is correct because the CPU utilization remains below 40% while concurrent requests have doubled, indicating that the existing replicas are not saturated on CPU but are bottlenecked by request queuing or thread contention. Vertex AI autoscaling scales based on CPU utilization by default; if the threshold is not crossed, new replicas are not provisioned, causing requests to queue and latency to spike. The engineer should verify the autoscaling configuration and consider scaling on request count or reducing the CPU utilization target.
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.
- ✗
Data skew causing longer inference time
Why it's wrong here
Data skew would affect prediction accuracy more than latency, and it would not explain the doubled concurrency with low CPU.
- ✗
Memory leak in the serving container
Why it's wrong here
A memory leak would typically cause memory utilization to increase over time and might eventually degrade performance, but CPU remains low and memory is not mentioned as high.
- ✓
Insufficient number of replicas for autoscaling
Why this is correct
If the number of replicas is not scaling fast enough to match increased concurrency, requests queue up, leading to higher latency while each replica's CPU is underutilized.
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.
- ✗
Model overfitting
Why it's wrong here
Overfitting reduces generalization but does not directly cause increased latency; it may cause incorrect predictions but not slower inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that low CPU utilization always means there is spare capacity, when in reality the bottleneck can be request queuing or thread pool exhaustion that does not raise CPU usage.
Detailed technical explanation
How to think about this question
Vertex AI Prediction uses Kubernetes-based autoscaling with a default CPU utilization target of 60%. When concurrent requests double but CPU stays below 40%, the autoscaler sees no need to add replicas, yet each replica's request queue grows, increasing tail latency due to head-of-line blocking. In practice, for I/O-bound or lightweight models, scaling on 'target concurrent requests' (via custom metrics) is more appropriate than CPU utilization to avoid this exact scenario.
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.
- →
Monitoring ML solutions — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Insufficient number of replicas for autoscaling — Option C is correct because the CPU utilization remains below 40% while concurrent requests have doubled, indicating that the existing replicas are not saturated on CPU but are bottlenecked by request queuing or thread contention. Vertex AI autoscaling scales based on CPU utilization by default; if the threshold is not crossed, new replicas are not provisioned, causing requests to queue and latency to spike. The engineer should verify the autoscaling configuration and consider scaling on request count or reducing the CPU utilization target.
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.
About these practice questions
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Last reviewed: Jun 30, 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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