- A
Enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds.
Model Monitoring automatically analyzes input distributions and prediction quality over time.
- B
Set up Cloud Logging to capture all prediction requests and responses for manual review.
Why wrong: Manual review is not proactive and may miss subtle drift.
- C
Randomly shuffle the training data before retraining to improve robustness.
Why wrong: Shuffling does not prevent drift detection.
- D
Schedule a monthly job to retrain the model with the latest data without monitoring.
Why wrong: Retraining without monitoring may not address drift if retraining schedule is not aligned.
Question 775 of 1,000
easyMultiple ChoiceObjective-mapped
Model Monitoring for Prediction Drift on Vertex AI
This PMLE practice question tests your understanding of prediction drift. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: prediction drift. 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 fraud detection model on Vertex AI Prediction. After three months, the model's accuracy has degraded, and the business is losing money due to undetected fraud. What should the team implement to proactively detect such issues?
Quick Answer
The answer is to enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds. This is correct because prediction drift occurs when the statistical distribution of incoming prediction requests shifts from the training data, silently degrading model performance without obvious errors. Vertex AI Model Monitoring continuously compares live prediction data against a baseline, automatically detecting such drift and triggering alerts so the team can investigate before business impact escalates. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that monitoring is a proactive MLOps practice, distinct from reactive logging or retraining—a common trap is confusing log analysis with drift detection, but logs only record raw data, not distribution shifts. Remember the mnemonic “Drift Demands Detection, Not Logs” to avoid choosing options that rely solely on logging or ad-hoc retraining.
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
✓
Enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds.
Option A is correct because Vertex AI Model Monitoring tracks prediction drift and alerts when metrics exceed thresholds, enabling proactive detection of model degradation. Option B is wrong because Cloud Logging captures requests and responses but does not automatically detect drift, requiring manual review. Option C is wrong because shuffling training data does not help detect drift. Option D is wrong because scheduling retraining without monitoring cannot proactively detect issues before they cause loss.
Key principle: Prediction drift
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 to track prediction drift and alert when metrics exceed thresholds.
Why this is correct
Model Monitoring automatically analyzes input distributions and prediction quality over time.
Related concept
Prediction drift
- ✗
Set up Cloud Logging to capture all prediction requests and responses for manual review.
Why it's wrong here
Manual review is not proactive and may miss subtle drift.
- ✗
Randomly shuffle the training data before retraining to improve robustness.
Why it's wrong here
Shuffling does not prevent drift detection.
- ✗
Schedule a monthly job to retrain the model with the latest data without monitoring.
Why it's wrong here
Retraining without monitoring may not address drift if retraining schedule is not aligned.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is to confuse monitoring with logging or retraining. Logging provides data but not automated drift detection; retraining fixes drift but doesn't detect it proactively.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Prediction drift
- Vertex AI Model Monitoring
- Proactive detection
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
Prediction drift
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. Prediction drift 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.
Review prediction drift, then practise related PMLE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PMLE question test?
Prediction drift
What is the correct answer to this question?
The correct answer is: Enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds. — Option A is correct because Vertex AI Model Monitoring tracks prediction drift and alerts when metrics exceed thresholds, enabling proactive detection of model degradation. Option B is wrong because Cloud Logging captures requests and responses but does not automatically detect drift, requiring manual review. Option C is wrong because shuffling training data does not help detect drift. Option D is wrong because scheduling retraining without monitoring cannot proactively detect issues before they cause loss.
What should I do if I get this PMLE question wrong?
Review prediction drift, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Prediction drift
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Last reviewed: Jun 24, 2026
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