- A
Re-deploy the model with a sampling rate of 100%
Why wrong: Sampling rate affects data capture but does not enable drift monitoring.
- B
Configure Vertex AI Model Monitoring to monitor prediction drift
Correct: Prediction drift monitoring is a built-in feature of Model Monitoring.
- C
Enable Vertex AI Explainability with XRAI on the endpoint deployment
Why wrong: Explainability is not required for prediction drift monitoring.
- D
Enable request/response logging to BigQuery and build custom drift detection
Why wrong: This is more complex but unnecessary; Model Monitoring provides drift detection out of the box.
Question 214 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 model deployed on a Vertex AI Endpoint uses an image model with XRAI explainability. The team notices that the prediction distributions are shifting over time. They want to monitor prediction drift. However, the explainability feature is not enabled. What must the engineer do to enable monitoring prediction drift?
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 Vertex AI Model Monitoring to monitor prediction drift
Prediction drift monitoring is part of Vertex AI Model Monitoring and does not require explainability to be enabled. It can be configured independently.
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.
- ✗
Re-deploy the model with a sampling rate of 100%
Why it's wrong here
Sampling rate affects data capture but does not enable drift monitoring.
- ✓
Configure Vertex AI Model Monitoring to monitor prediction drift
Why this is correct
Correct: Prediction drift monitoring is a built-in feature of Model Monitoring.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Vertex AI Explainability with XRAI on the endpoint deployment
Why it's wrong here
Explainability is not required for prediction drift monitoring.
- ✗
Enable request/response logging to BigQuery and build custom drift detection
Why it's wrong here
This is more complex but unnecessary; Model Monitoring provides drift detection out of the box.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 Vertex AI Model Monitoring to monitor prediction drift — Prediction drift monitoring is part of Vertex AI Model Monitoring and does not require explainability to be enabled. It can be configured independently.
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.
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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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