PMLE Monitoring ML Solutions Practice Question
A company deploys a model on Vertex AI Endpoints and configures Vertex AI Model Monitoring with a sampling rate of 0.1 and monitoring frequency of every hour. They notice that the monitoring alert fires only after several hours of drift. What is the most likely cause?
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
✓
The sampling rate is too low, causing the drift detection algorithm to require more time to accumulate a representative distribution
A low sampling rate (0.1) means only 10% of predictions are analyzed, which reduces the statistical power to detect drift quickly. Increasing the sampling rate improves detection speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The alerting threshold is set too high; it should be lowered
Why it's wrong here
Threshold affects when alert fires, but the primary delay is due to insufficient sample size.
- ✗
The monitoring frequency is too low; it should be every minute
Why it's wrong here
Even with high frequency, the low sampling rate causes detection delay.
- ✗
The model endpoint is not receiving enough traffic to generate a statistically significant sample
Why it's wrong here
Low traffic could contribute, but the primary issue is the sampling rate.
- ✓
The sampling rate is too low, causing the drift detection algorithm to require more time to accumulate a representative distribution
Why this is correct
Correct: low sampling rate delays detection.
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Same concept, more angles
2 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An ML engineer has set up Vertex AI Model Monitoring on an endpoint with a sampling rate of 0.1 (10%). They notice that the monitoring job runs hourly but the reported drift metrics seem inconsistent. What is the most likely cause?
medium- ✓ A.The sampling rate is too low, leading to insufficient data for reliable drift statistics.
- B.Prediction drift monitoring is not enabled; only feature drift is configured.
- C.The drift detection algorithm is not suited for this model; try changing from JS divergence to L-infinity distance.
- D.The monitoring frequency is too low; it should be set to every 5 minutes.
Why A: A low sampling rate means only a small fraction of requests are logged for monitoring, which can lead to statistical noise and inconsistent drift metrics. Increasing the sampling rate would improve accuracy.
Variation 2. 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)
medium- A.Model version
- B.Explanation method
- ✓ C.Sampling rate
- D.Alerting threshold
- ✓ E.Monitoring frequency
Why C: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.