PMLE Monitoring ML Solutions Practice Question
An ML engineer is configuring Vertex AI Model Monitoring for drift detection on a deployed endpoint. Which TWO settings directly affect the frequency and accuracy of drift detection? (Choose 2)
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
✓
Sampling rate
Sampling rate controls what fraction of predictions is analyzed; monitoring frequency controls how often the distribution comparison is performed. Both directly impact detection speed and accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model version
Why it's wrong here
Model version identifies which deployed model is monitored; it does not set the monitoring interval or the drift threshold that govern detection frequency and accuracy. It is tempting because versioning matters when comparing a candidate against a baseline, and it would be correct when the task is selecting which model's predictions to evaluate.
- ✗
Explanation method
Why it's wrong here
Explanation method configures feature attributions for prediction explanations, not drift detection, so it changes neither the monitoring schedule nor the statistical comparison. It is tempting because it is a genuine Vertex AI Model Monitoring setting, and it would be the correct choice when the requirement is explaining individual predictions rather than detecting distribution drift.
- ✓
Sampling rate
Why this is correct
Sampling rate determines what proportion of prediction requests are logged and analysed, directly governing how much data feeds the drift baseline. A higher rate improves detection accuracy for low-traffic endpoints, while a lower rate reduces cost but risks missing subtle distribution shifts.
- ✗
Alerting threshold
Why it's wrong here
Alerting threshold sets when a notification fires, not how often monitoring runs or how accurately drift is measured; those come from the monitoring schedule and drift thresholds. It is tempting because it tunes sensitivity to detected drift, and it would be correct when configuring alerting behaviour rather than detection cadence.
- ✓
Monitoring frequency
Why this is correct
Monitoring frequency sets how often Vertex AI evaluates logged predictions against the baseline, directly controlling how quickly drift is detected. Shorter intervals catch shifts sooner but raise compute cost; longer intervals delay alerts, affecting the timeliness of drift detection.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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