- A
Deploy a new model version immediately
Why wrong: Deploying a new model may not address the root cause if drift is due to data changes.
- B
Use Vertex AI Explainability to compute feature attributions and identify drifted features
Correct: Explainability provides feature importance, helping to pinpoint which features are driving drift.
- C
Check the model's confusion matrix in BigQuery
Why wrong: Confusion matrix is for model quality monitoring, not feature drift analysis.
- D
Increase the sampling rate to capture more data
Why wrong: Sampling rate affects detection sensitivity but does not identify which features are responsible.
Question 244 of 1,000
PMLE Monitoring ML Solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company has deployed a model to Vertex AI Endpoints and wants to monitor for feature drift using Jensen-Shannon divergence. They have set a threshold of 0.1. After one week, the monitoring job reports a divergence of 0.15 for a feature. What should the engineer do next to diagnose which features are contributing to the 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
✓
Use Vertex AI Explainability to compute feature attributions and identify drifted features
To identify which features are drifting, the engineer can use Vertex AI Explainability to compute feature attributions (e.g., SHAP values) and correlate them with drift 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.
- ✗
Deploy a new model version immediately
Why it's wrong here
Deploying a new model may not address the root cause if drift is due to data changes.
- ✓
Use Vertex AI Explainability to compute feature attributions and identify drifted features
Why this is correct
Correct: Explainability provides feature importance, helping to pinpoint which features are driving drift.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Check the model's confusion matrix in BigQuery
Why it's wrong here
Confusion matrix is for model quality monitoring, not feature drift analysis.
- ✗
Increase the sampling rate to capture more data
Why it's wrong here
Sampling rate affects detection sensitivity but does not identify which features are responsible.
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.
Trap categories for this question
Similar concept trap
Confusion matrix is for model quality monitoring, not feature drift analysis.
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
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 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: Use Vertex AI Explainability to compute feature attributions and identify drifted features — To identify which features are drifting, the engineer can use Vertex AI Explainability to compute feature attributions (e.g., SHAP values) and correlate them with drift metrics.
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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