PMLE Monitoring ML Solutions Practice Question
An MLOps engineer is configuring Vertex AI Model Monitoring for a deployed model. They want to monitor for feature skew between training and serving data, but only for a subset of features. The training data has 100 features, and they want to monitor only the top 10 most important features to reduce cost and noise. How can they achieve this?
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
✓
Use the 'feature_names' parameter in the ModelMonitoringObjectConfig to specify which features to monitor
Vertex AI Model Monitoring allows you to specify a list of feature names to monitor via the 'feature_names' attribute in the monitoring configuration. This can be set when creating the monitoring job, targeting only the features of interest.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the 'monitoring_interval' to a low value so that only frequent features are monitored
Why it's wrong here
Monitoring interval does not control which features are monitored.
- ✗
Train a new model with only the top 10 features and redeploy it
Why it's wrong here
Redeploying a model is a costly and unnecessary workaround.
- ✓
Use the 'feature_names' parameter in the ModelMonitoringObjectConfig to specify which features to monitor
Why this is correct
The feature_names parameter allows you to select a subset of features for monitoring.
- ✗
Set the 'sampling_rate' to 100% and ignore the rest
Why it's wrong here
Sampling rate controls the fraction of requests logged, not which features are monitored.
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