- A
Enable Vertex AI Model Monitoring with sampling rate 100%
Why wrong: Model Monitoring does not capture full request/response payloads; it captures feature values for skew/drift analysis.
- B
Export logs to Cloud Storage using a sink and use gcloud logging read
Why wrong: This captures only log text, not the full request/response payloads needed for latency analysis.
- C
Use Cloud Monitoring custom metrics to capture latency per request
Why wrong: Custom metrics can monitor latency but not capture request/response payloads for root cause analysis.
- D
Configure request/response logging by specifying a BigQuery destination in the endpoint deployment
Correct: Vertex AI Endpoints can log request/response to BigQuery when enabled in the deployment.
Question 887 of 1,000
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 data scientist notices that the model's prediction latency has increased over the last week. They need to investigate the root cause by examining request and response logs for the Vertex AI Endpoint. What is the recommended way to capture these logs?
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
✓
Configure request/response logging by specifying a BigQuery destination in the endpoint deployment
Vertex AI Endpoint can be configured to log request/response data to BigQuery via a log sink. This data can then be analyzed to understand latency issues.
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 Vertex AI Model Monitoring with sampling rate 100%
Why it's wrong here
Model Monitoring does not capture full request/response payloads; it captures feature values for skew/drift analysis.
- ✗
Export logs to Cloud Storage using a sink and use gcloud logging read
Why it's wrong here
This captures only log text, not the full request/response payloads needed for latency analysis.
- ✗
Use Cloud Monitoring custom metrics to capture latency per request
Why it's wrong here
Custom metrics can monitor latency but not capture request/response payloads for root cause analysis.
- ✓
Configure request/response logging by specifying a BigQuery destination in the endpoint deployment
Why this is correct
Correct: Vertex AI Endpoints can log request/response to BigQuery when enabled in the deployment.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Configure request/response logging by specifying a BigQuery destination in the endpoint deployment — Vertex AI Endpoint can be configured to log request/response data to BigQuery via a log sink. This data can then be analyzed to understand latency issues.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 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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