PMLE Monitoring ML Solutions Practice Question

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?

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, leading to insufficient data for reliable drift statistics.

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.

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 sampling rate is too low, leading to insufficient data for reliable drift statistics.
Why this is correct
10% sampling may result in small sample sizes, causing high variance in drift estimates.
✗
Prediction drift monitoring is not enabled; only feature drift is configured.
Why it's wrong here
The scenario mentions drift metrics, but the issue is sample size, not missing prediction drift.
✗
The drift detection algorithm is not suited for this model; try changing from JS divergence to L-infinity distance.
Why it's wrong here
The algorithm is not the primary cause; low sample size causes inconsistency.
✗
The monitoring frequency is too low; it should be set to every 5 minutes.
Why it's wrong here
Hourly frequency is typical; the issue is sampling rate, not frequency.

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PMLE Monitoring ML Solutions Practice Question

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