PMLE Monitoring ML Solutions Practice Question
An ML engineer is using Vertex AI Model Monitoring on a deployed model that predicts customer churn. The model's input features include a mix of numerical and categorical features. The engineer wants to detect changes in the distribution of a specific categorical feature, 'contract_type', which has 20 possible values. Which approach should be used to effectively monitor drift for this feature?
⚠ Common exam trap
The trap here is assuming that default monitoring across all features is sufficient, without considering the need to tune thresholds for high-cardinality categorical features.
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
✓
Configure drift detection specifically for 'contract_type' and set an appropriate threshold based on its cardinality.
Vertex AI Model Monitoring allows per-feature drift detection with customizable thresholds. For categorical features with many categories, the default threshold may not be optimal; tuning the threshold based on cardinality helps balance sensitivity and false positives. By explicitly monitoring 'contract_type', the engineer can detect shifts in its distribution, which is crucial for model performance if that feature is important.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure drift detection specifically for 'contract_type' and set an appropriate threshold based on its cardinality.
Why this is correct
Vertex AI Model Monitoring supports feature-level drift detection. For categorical features with multiple values, you can set a specific threshold that accounts for the feature's cardinality. By focusing on 'contract_type' and tuning the threshold, the engineer can effectively detect meaningful distribution shifts without being overwhelmed by noise from other features. This targeted approach is best for monitoring a specific high-cardinality categorical feature.
- ✗
Use Vertex AI Explainable AI to compute feature attributions and monitor changes in attributions for 'contract_type'.
Why it's wrong here
Explainable AI provides feature attributions for predictions, which indicate feature importance but not distribution drift. Monitoring changes in attributions does not directly detect whether the distribution of 'contract_type' has shifted. Drift detection should be based on the feature's value distribution, not its contribution to predictions. This method is not suitable for detecting data drift of a specific feature.
- ✗
Convert 'contract_type' into a numerical feature using one-hot encoding and then monitor it as a numerical feature.
Why it's wrong here
One-hot encoding would create 20 binary features, each with low frequency. Monitoring each binary feature separately would be inefficient and may not capture the overall distribution shift of the original categorical feature. Moreover, Vertex AI Model Monitoring natively supports categorical features, so conversion is unnecessary and could complicate interpretation. This approach does not effectively monitor the original feature's distribution.
- ✗
Enable drift detection for all features and use the default threshold.
Why it's wrong here
Enabling drift detection for all features with default thresholds may work, but categorical features with many values require special handling. Default thresholds might not be appropriate for high-cardinality categorical features, leading to either missed drifts or false alarms. This approach does not specifically address the challenge of monitoring a 20-value categorical feature and may not provide effective detection.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.