PMLE Monitoring ML Solutions Practice Question
You are an MLOps engineer at a retail company. Your team has deployed a demand forecasting model to a Vertex AI Endpoint. The model uses 12 numerical features and outputs a single numeric value representing predicted units sold. You have configured Vertex AI Model Monitoring with a training dataset that includes the full feature schema and prediction distribution. After a week, you observe that the feature 'promotion_flag' has a Jensen-Shannon divergence of 0.15, while all other features remain below 0.05. The model's prediction distribution has also shifted. Which action should you take first to diagnose the cause of the drift?
⚠ Common exam trap
The trap here is assuming that any drift requires immediate retraining, when in fact the first step should be to diagnose the cause of the drift to ensure the right corrective action.
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
✓
Examine the distribution of the 'promotion_flag' feature in the recent serving data and compare it with the training distribution to determine if the feature's meaning or data collection process has changed.
The feature 'promotion_flag' has the highest drift, so examining its recent distribution compared to training helps identify if the feature's meaning or data collection has changed. This diagnostic step is crucial before retraining or adjusting monitoring, as it reveals whether the drift is due to a data pipeline issue or a genuine change in the underlying pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Examine the distribution of the 'promotion_flag' feature in the recent serving data and compare it with the training distribution to determine if the feature's meaning or data collection process has changed.
Why this is correct
The 'promotion_flag' feature shows the highest divergence, indicating a significant shift. Comparing recent serving data distribution to the training distribution helps identify whether the feature's meaning or data collection process has changed. This diagnostic step is appropriate before considering retraining or other actions, as it pinpoints the root cause of drift and informs subsequent mitigation steps.
- ✗
Increase the monitoring frequency to 15 minutes and lower the drift threshold for 'promotion_flag' to 0.05 to get more granular alerts.
Why it's wrong here
Adjusting monitoring frequency and thresholds may improve alerting sensitivity but does not help diagnose the cause of the observed drift. The question asks for the first action to diagnose the cause, not to enhance monitoring. Changing thresholds could generate more alerts but does not provide insight into why the feature distribution has shifted.
- ✗
Ignore the drift because a Jensen-Shannon divergence of 0.15 is below the typical threshold of 0.2 used for numerical features.
Why it's wrong here
A Jensen-Shannon divergence of 0.15 is relatively high for a feature and should not be ignored. While thresholds can vary, a divergence of 0.15 often indicates significant drift. Ignoring it could lead to degraded model performance. The appropriate action is to investigate the cause rather than dismiss the signal based on a presumed threshold.
- ✗
Immediately trigger a retraining pipeline using the most recent data to update the model and reduce the drift.
Why it's wrong here
Triggering retraining without understanding the cause of the drift may lead to a model that perpetuates underlying data issues. The high divergence in 'promotion_flag' suggests a specific problem that should be diagnosed first. Retraining is a mitigation step, but it is not the first action to take when diagnosing drift, as it may not address the root cause.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.