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PMLE A company uses Vertex AI Model Monitoring Practice Question
A company uses Vertex AI Model Monitoring. Which two configuration options can be set to reduce false positive drift alerts?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing sensitivity (lowering thresholds or shortening windows) reduces false positives, when in fact the opposite is true—these actions increase alert volume and false positives.
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 a sample percentage of predictions
Option A (Use a sample percentage of predictions) is correct because sampling only a subset of incoming prediction requests for drift analysis reduces the volume of data evaluated, smoothing out minor statistical fluctuations that would otherwise trigger spurious drift alerts. Option C (Increase the drift threshold) is correct because raising the threshold means the observed drift must be larger before an alert fires, directly filtering out small, benign deviations and lowering false positives. Option B (Set a shorter alerting window) is wrong because a shorter window captures less data and is more sensitive to noise, which typically increases rather than reduces false alerts. Option D (Decrease the drift threshold) is wrong because a lower threshold makes alerts fire on smaller deviations, producing more false positives. Option E (Enable feature attribution monitoring) is wrong because feature attribution explains which features drive predictions; it is an explainability capability, not a mechanism for reducing false positive drift alerts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a sample percentage of predictions
Why this is correct
Monitoring only a sample percentage of predictions reduces the volume of data compared against the baseline, dampening statistical noise that triggers spurious drift alerts. This satisfies the goal of lowering false positives without disabling monitoring entirely.
- ✗
Set a shorter alerting window
Why it's wrong here
A shorter alerting window evaluates fewer data points, so transient fluctuations trigger alerts sooner, increasing false positives. It tempts when seeking faster detection, but reducing false positives requires lengthening the window so drift must persist before alerting.
- ✓
Increase the drift threshold
Why this is correct
Raising the drift threshold directly reduces false positive alerts, since Vertex AI Model Monitoring only flags drift when the computed distance between the baseline and current distribution exceeds that configured value. A higher threshold tolerates more statistical deviation before triggering an alert, satisfying the requirement to suppress spurious notifications.
- ✗
Decrease the drift threshold
Why it's wrong here
Lowering the drift threshold makes the monitor flag smaller deviations, so benign variation exceeds it and alerts fire more often. It tempts as tightening sensitivity, yet reducing false positives requires raising the threshold above normal fluctuation.
- ✗
Enable feature attribution monitoring
Why it's wrong here
Feature attribution explains which features drove a prediction; it neither suppresses nor filters drift alerts. It tempts because it enriches monitoring insight, but the drift threshold and alerting window are the settings that govern false positive volume.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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