PMLE Monitoring ML Solutions Practice Question
A credit-risk team runs a tabular model on a Vertex AI Endpoint. They configured Vertex AI Model Monitoring with a training dataset and skew detection using the default threshold. After a week, they receive alerts that many features have high training-serving skew, but the model's business metrics (approval rate, default rate) are unchanged. They suspect the alerts are false positives due to a recent change in an upstream data pipeline that shifted feature distributions. What should they do to reduce these false alerts while still monitoring for real skew?
⚠ Common exam trap
The trap here is assuming that any skew alert must be suppressed by disabling monitoring or tightening thresholds, rather than recognizing that the training baseline itself may need to be refreshed after a legitimate pipeline change.
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
✓
Recreate the monitoring job with a new training dataset that reflects the recent pipeline change, and adjust the skew threshold based on observed variance.
Training-serving skew compares live data to the training baseline. When an upstream pipeline changes distributions legitimately, the old baseline becomes invalid and triggers false skew alerts. Updating the monitoring configuration with a representative training dataset and a threshold based on observed variance realigns detection with current production behavior. Disabling monitoring or making it more sensitive does not solve the underlying baseline mismatch.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recreate the monitoring job with a new training dataset that reflects the recent pipeline change, and adjust the skew threshold based on observed variance.
Why this is correct
Training-serving skew compares live serving inputs to the statistics of the training dataset. If the upstream pipeline legitimately changed feature distributions, the original training baseline is stale and will flag normal data as skewed. Updating the baseline to include recent representative data and setting a threshold that accounts for observed variance restores accurate detection. This aligns monitoring with the current production reality without disabling protection.
- ✗
Disable skew detection and rely only on prediction drift monitoring.
Why it's wrong here
Disabling skew detection removes an important safeguard that catches upstream data issues before they affect predictions. Prediction drift alone may not detect subtle input changes that could later degrade model performance. The team wants to keep monitoring for real skew, so turning it off is an overreaction. A better approach is to update the baseline and threshold so the detector remains useful without false alarms.
- ✗
Switch from skew detection to outlier detection and set the outlier threshold to a very low value.
Why it's wrong here
Outlier detection identifies individual anomalous values, not distribution shifts relative to training. It does not address the systematic shift caused by the pipeline change. Setting a very low outlier threshold would likely generate even more alerts, increasing noise. The scenario is about training-serving skew, so the correct fix is to refresh the training baseline and tune the skew threshold, not replace the detection type.
- ✗
Increase the monitoring frequency and lower the skew threshold to capture more data points.
Why it's wrong here
Increasing frequency and lowering the threshold makes the detector more sensitive, which would produce more alerts, not fewer. The team already has too many false positives, so this action worsens the problem. It does not address the root cause that the upstream pipeline change shifted distributions. Vertex AI Model Monitoring thresholds and windows should be tuned based on expected variance, not tightened arbitrarily when false alerts are already noisy.
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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.