The answer is that an accelerator count of 0 is the most likely cause of high latency on a Vertex AI endpoint. When the accelerator count is set to 0, the endpoint defaults to running inference exclusively on the CPU, which lacks the parallel processing power needed for deep learning models, especially under moderate traffic where compute demands quickly exceed CPU throughput. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI endpoint configuration and the critical role of hardware accelerators like GPUs or TPUs in production inference. A common trap is assuming latency issues stem from model size or data preprocessing, but the simplest check is always the accelerator count. Remember the mnemonic: Zero GPU, zero speed—always verify your accelerator count before tuning anything else.
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 ML engineer notices that predictions are taking longer than expected under moderate traffic. Reviewing the endpoint configuration, what is the most likely cause of the high latency?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The accelerator count is 0, meaning no GPU is used.
When the accelerator count is set to 0, the endpoint runs inference on the CPU only, which is significantly slower than GPU-accelerated inference for deep learning models. This is the most direct cause of high latency under moderate traffic, as the model's compute demands exceed CPU throughput.
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.
✗
Container logging is disabled, slowing down request processing.
Why it's wrong here
Disabling container logging reduces overhead and does not slow down predictions; it may improve performance.
✓
The accelerator count is 0, meaning no GPU is used.
Why this is correct
BERT models are computationally intensive and benefit greatly from GPU acceleration; without it, inference is CPU-bound and slow.
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 machine type n1-standard-4 is underpowered for the model's compute needs.
Why it's wrong here
While possible, the primary bottleneck for a BERT model is the lack of GPU acceleration; a CPU-only machine will be significantly slower.
✗
Automatic scaling is set with a maxReplicaCount of 10, which creates overhead.
Why it's wrong here
Autoscaling overhead is negligible and does not cause high latency; it actually helps handle traffic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that CPU machine type is the primary cause of latency, when in fact the accelerator count being zero is the more direct and common misconfiguration for deep learning models.
Detailed technical explanation
How to think about this question
In Vertex AI Prediction, the accelerator count field specifies the number of GPUs attached to each replica; setting it to 0 forces all inference to run on the CPU, which can be 10-100x slower for deep learning models due to lack of parallel matrix operations. Under moderate traffic, CPU-based inference quickly saturates, causing request queuing and increased tail latency. Real-world scenarios often involve models like BERT or ResNet where GPU acceleration is essential for sub-second predictions.
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 — 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 accelerator count is 0, meaning no GPU is used. — When the accelerator count is set to 0, the endpoint runs inference on the CPU only, which is significantly slower than GPU-accelerated inference for deep learning models. This is the most direct cause of high latency under moderate traffic, as the model's compute demands exceed CPU throughput.
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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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.
Question Discussion
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