- A
There is a network bottleneck
Why wrong: Network bottlenecks would manifest differently.
- B
The model size is too large for the machine type
Why wrong: This would cause errors during deployment, not scaling issues.
- C
The endpoint is using a global load balancer
Why wrong: Global load balancing is generally not the cause of high latency.
- D
The autoscaling metric is based on CPU utilization but the model is GPU-bound
GPU-bound models require GPU-based metrics for effective autoscaling.
Question 28 of 997
Vertex AI Endpoint Autoscaling: High Latency Causes
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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.
During a load test, a Vertex AI endpoint serving a large language model experiences high latency and increased error rates. The endpoint is configured with autoscaling. 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.
Quick Answer
The answer is that the autoscaling metric is based on CPU utilization but the model is GPU-bound, which is the most likely cause of high latency and increased error rates during your load test. This happens because Vertex AI endpoint autoscaling relies on the configured metric to trigger new replicas; when the metric is CPU utilization but the actual bottleneck is GPU compute, the scaling signal never fires, leaving the endpoint under-provisioned as requests pile up. On the Google Cloud Generative AI Leader exam, this question tests your understanding that large language models are typically GPU-bound, so CPU-based autoscaling creates a dangerous mismatch—a common trap where candidates assume any metric will work. The key insight is that the scaling metric must reflect the true resource constraint. Memory tip: “GPU grinds, CPU sleeps—if you scale on the wrong metric, the latency creeps.”
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 autoscaling metric is based on CPU utilization but the model is GPU-bound
Option D is correct because when a model is GPU-bound, CPU utilization remains low even under heavy inference load, so autoscaling based on CPU metrics fails to trigger additional replicas. This leads to queued requests, increased latency, and eventual error rates as the existing GPU instances become saturated. Vertex AI endpoints default to CPU-based autoscaling unless explicitly configured with GPU metrics like 'gpu_utilization' or custom metrics.
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.
- ✗
There is a network bottleneck
Why it's wrong here
Network bottlenecks would manifest differently.
- ✗
The model size is too large for the machine type
Why it's wrong here
This would cause errors during deployment, not scaling issues.
- ✗
The endpoint is using a global load balancer
Why it's wrong here
Global load balancing is generally not the cause of high latency.
- ✓
The autoscaling metric is based on CPU utilization but the model is GPU-bound
Why this is correct
GPU-bound models require GPU-based metrics for effective autoscaling.
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume autoscaling always works generically, but Vertex AI's default CPU-based metric is irrelevant for GPU-accelerated inference, causing silent failures under load.
Detailed technical explanation
How to think about this question
GPU-bound models (e.g., large transformers) spend most inference time on matrix multiplications executed on GPU, leaving CPU idle. Vertex AI's default autoscaling uses 'cpu_utilization' target (default 60%), which never triggers scale-up when GPU is the bottleneck. To fix this, you must configure custom metrics via Cloud Monitoring (e.g., 'nvidia/gpu_utilization') or use request-based autoscaling with 'target_requests_per_second' to match GPU throughput.
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
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The autoscaling metric is based on CPU utilization but the model is GPU-bound — Option D is correct because when a model is GPU-bound, CPU utilization remains low even under heavy inference load, so autoscaling based on CPU metrics fails to trigger additional replicas. This leads to queued requests, increased latency, and eventual error rates as the existing GPU instances become saturated. Vertex AI endpoints default to CPU-based autoscaling unless explicitly configured with GPU metrics like 'gpu_utilization' or custom metrics.
What should I do if I get this Generative AI Leader 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: Jul 4, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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