The answer is to use the 'compute.googleapis.com/accelerator/memory_utilization' metric with a metric threshold condition. This is the most efficient approach because Vertex AI Training jobs automatically export this metric to Cloud Monitoring via the Google Cloud agent running on the training VM, requiring zero custom instrumentation or log parsing. On the Google Professional Machine Learning Engineer exam, this tests your understanding of native integration between Vertex AI and Cloud Monitoring, often appearing as a trap where incorrect options suggest complex custom scripts or log-based alerts. A common mistake is to over-engineer the solution with custom exporters, but the key insight is that GPU memory utilization is a first-class, pre-collected metric. Remember the mnemonic: "No code for the load" — the metric is already loaded into Monitoring, so you just set the threshold.
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.
An MLOps team wants to set up alerts for GPU memory utilization on Vertex AI Training jobs. Which approach is most efficient?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the 'compute.googleapis.com/accelerator/memory_utilization' metric with a metric threshold condition.
Option D is correct because Vertex AI training jobs automatically export the 'compute.googleapis.com/accelerator/memory_utilization' metric to Cloud Monitoring. This metric is natively collected by the Google Cloud agent on the training VM, so you can directly create a metric threshold alert without any custom instrumentation or log parsing. It is the most efficient approach as it requires zero additional code or configuration.
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.
✗
Enable Cloud Audit Logs for the training job and parse the logs for GPU memory events.
Why it's wrong here
Audit logs don't track GPU memory.
✗
Create a log-based metric from the training job's GPU logs.
Why it's wrong here
Logs may not contain structured GPU memory data.
✗
Add a container sidecar that emits a custom metric for GPU memory usage via OpenCensus.
Why it's wrong here
More effort than using existing metrics.
✓
Use the 'compute.googleapis.com/accelerator/memory_utilization' metric with a metric threshold condition.
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that custom instrumentation (sidecars or log parsing) is always required for GPU monitoring, when in fact Vertex AI provides a native metric that eliminates that need.
Detailed technical explanation
How to think about this question
The 'compute.googleapis.com/accelerator/memory_utilization' metric is part of the Google Cloud Operations suite's agent-based monitoring, which collects GPU metrics via the NVIDIA Management Library (NVML) on the host. This metric is sampled every 60 seconds by default and can be used in alerting policies with conditions like 'metric absent' or 'threshold violations'. In a real-world scenario, if you need sub-minute granularity, you might still use a sidecar, but for standard alerting, the built-in metric is the simplest and most cost-effective choice.
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 — 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: Use the 'compute.googleapis.com/accelerator/memory_utilization' metric with a metric threshold condition. — Option D is correct because Vertex AI training jobs automatically export the 'compute.googleapis.com/accelerator/memory_utilization' metric to Cloud Monitoring. This metric is natively collected by the Google Cloud agent on the training VM, so you can directly create a metric threshold alert without any custom instrumentation or log parsing. It is the most efficient approach as it requires zero additional code or configuration.
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.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.