PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?
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
✓
Enable feature monitoring on the feature group with drift threshold and notification channel.
Feature monitoring in Vertex AI Feature Store allows defining drift thresholds and alerting via Cloud Monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable feature monitoring on the feature group with drift threshold and notification channel.
Why this is correct
Feature group monitoring computes drift statistics against a baseline distribution and triggers alerts via a configured notification channel when the drift threshold is exceeded. Enabling it at the feature group level with both parameters satisfies the automated-alert requirement without custom pipeline code.
- ✗
Use Cloud Monitoring custom metrics and log-based alerts manually.
Why it's wrong here
Custom metrics and log-based alerts must be built and tuned by the team, and they detect only what is explicitly instrumented, not statistical distribution shift. This suits bespoke operational telemetry; feature drift needs the managed skew and drift detection that compares distributions to a baseline automatically.
- ✗
Use Vertex AI Experiments to compare distributions.
Why it's wrong here
Experiments compare runs and metrics for model evaluation, not continuous feature distributions, so no automatic alert triggers on drift. It suits tracking training iterations and hyperparameters; monitoring distribution deviation against a baseline requires Vertex AI Feature Store's drift detection configuration.
- ✗
Export features to BigQuery and set up scheduled queries with alerts.
Why it's wrong here
Scheduled BigQuery queries add latency and require custom comparison logic, so alerts fire after the drift window rather than automatically. Export suits ad-hoc analysis or reporting; Vertex AI Feature Store's native drift monitoring computes distribution deviation against the baseline and raises alerts directly.
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