mediumMultiple Choice
PDE Practice Question: You configured a model deployment monitor on your…
Exhibit
Refer to the exhibit.
```
$ gcloud ai endpoints describe my-endpoint
...
modelDeploymentMonitors:
- model: projects/my-project/models/my-model
objectiveConfig:
objectiveType: skew
skewConfig:
featureSkewThresholds:
age: 0.3
income: 0.2
alertConfig:
enableAlerting: true
alertEmailAddresses:
- admin@example.com
```You configured a model deployment monitor on your Vertex AI endpoint as shown. What will happen when the feature 'age' has a skew of 0.4?
⚠ Common exam trap
Google Cloud often tests the misconception that alerts require multiple features to exceed thresholds or that the system can automatically roll back models, when in reality each feature is evaluated independently and only notifications are sent.
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
✓
An alert will be sent to admin@example.com
The monitoring configuration shows an alert threshold of 0.3 for the feature 'age', and a skew of 0.4 exceeds that threshold. Vertex AI Model Monitoring will trigger the configured alert action, which in this case is sending an email to admin@example.com. The alert is based on the specific feature's threshold, not on any other feature's threshold.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
An alert will be sent to admin@example.com
Why this is correct
The deployment monitor's configured skew threshold is exceeded by the 0.4 value, triggering the notification action bound to that monitor. Because admin@example.com is the registered alert recipient, the breach fires an email alert to that address.
- ✗
The endpoint will automatically roll back to a previous model version
Why it's wrong here
Model deployment monitors only emit alerts; they do not trigger rollbacks. Rollback requires a separate deployment or traffic-splitting mechanism. Monitoring exists to surface skew and drift signals for human or automated response, not to mutate the served model version.
- ✗
No alert will be sent because the skew threshold is 0.2 for income
Why it's wrong here
Skew thresholds are configured per feature, so the 0.2 threshold on 'income' does not govern 'age'. If age's own threshold is 0.2, a skew of 0.4 breaches it and fires an alert. Per-feature thresholds exist precisely so each monitored feature is evaluated independently.
- ✗
An alert will be sent only if both features exceed their thresholds
Why it's wrong here
Vertex AI evaluates each feature's skew against its own threshold independently; alerts fire per feature, not jointly. Requiring both to breach would suppress genuine drift on a single feature. Independent per-feature evaluation is the correct model for skew monitoring.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.