- A
Configure Vertex AI Model Monitoring to detect feature drift and alert when metrics exceed thresholds.
Vertex AI Model Monitoring directly monitors for drift and skew, which helps detect accuracy decline.
- B
Create a Cloud Monitoring alert for prediction response count.
Why wrong: Prediction count is not a quality metric.
- C
Use BigQuery ML to retrain the model more frequently.
Why wrong: This addresses retraining cadence, not monitoring.
- D
Set up a Cloud Monitoring uptime check on the prediction endpoint.
Why wrong: A uptime check only verifies availability, not prediction quality.
Question 187 of 1,000
mediumMultiple ChoiceObjective-mapped
Early Detection of Accuracy Decline
This PMLE practice question tests your understanding of pmle exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your team has a production ML model on Vertex AI that shows a gradual decline in accuracy over the past week. The model is retrained weekly using the latest data. Which monitoring approach should you implement to detect the issue earlier?
Quick Answer
The answer is to configure Vertex AI Model Monitoring to detect feature drift and alert when metrics exceed thresholds. This is correct because a gradual accuracy decline in production often stems from training-serving skew or data drift, where the distribution of incoming features shifts away from the training data, and Vertex AI Model Monitoring is specifically designed to track these statistical changes over time. On the Google Professional Machine Learning Engineer exam, this question tests your understanding that monitoring raw prediction counts or relying on Cloud Monitoring without custom drift metrics will miss the root cause, while BigQuery ML is irrelevant for real-time drift detection. A common trap is assuming any alerting tool works—remember that accuracy decline is a symptom, not the cause. Memory tip: think “drift before lift”—detect feature drift first to catch accuracy drops before they compound.
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 detect feature drift and alert when metrics exceed thresholds.
Option A is correct because Vertex AI Model Monitoring can detect feature drift and training-serving skew, which are common causes of accuracy decline. By alerting when drift metrics exceed thresholds, the team can identify issues before they significantly impact performance. Option B is incorrect; prediction response count is a volume metric and does not reflect model quality. Option C is incorrect; BigQuery ML retraining is not a monitoring solution. Option D is incorrect; uptime checks only verify endpoint availability, not accuracy.
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.
- ✓
Configure Vertex AI Model Monitoring to detect feature drift and alert when metrics exceed thresholds.
Why this is correct
Vertex AI Model Monitoring directly monitors for drift and skew, which helps detect accuracy decline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a Cloud Monitoring alert for prediction response count.
Why it's wrong here
Prediction count is not a quality metric.
- ✗
Use BigQuery ML to retrain the model more frequently.
Why it's wrong here
This addresses retraining cadence, not monitoring.
- ✗
Set up a Cloud Monitoring uptime check on the prediction endpoint.
Why it's wrong here
A uptime check only verifies availability, not prediction quality.
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
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?
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 detect feature drift and alert when metrics exceed thresholds. — Option A is correct because Vertex AI Model Monitoring can detect feature drift and training-serving skew, which are common causes of accuracy decline. By alerting when drift metrics exceed thresholds, the team can identify issues before they significantly impact performance. Option B is incorrect; prediction response count is a volume metric and does not reflect model quality. Option C is incorrect; BigQuery ML retraining is not a monitoring solution. Option D is incorrect; uptime checks only verify endpoint availability, not accuracy.
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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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 →
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Last reviewed: Jun 24, 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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