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PMLE Practice Question: Using Vertex AI continuous evaluation (model…
You are using Vertex AI continuous evaluation (model monitoring) for your deployed model. You receive an alert that the prediction distribution is significantly different from the training distribution. What should you do first?
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
✓
Analyze the input data to understand if there is a skew or drift.
When a monitoring alert triggers, the first step is to investigate the root cause: check if input data has changed, retraining is needed, or there is a data pipeline issue. Simply rolling back or retraining without analysis might be premature.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Roll back the model to the previous version immediately.
Why it's wrong here
Rolling back discards a model that may still be valid; distribution drift is a signal to investigate, not proof of degradation. First diagnose the cause — data pipeline changes, upstream schema shifts or genuine concept drift — before acting. Rollback suits a confirmed regression in live prediction quality, not an unverified drift alert.
- ✗
Increase the alerting threshold to reduce false positives.
Why it's wrong here
Raising the threshold suppresses the alert without addressing the underlying distribution shift, hiding genuine model degradation. It is tempting when alerts appear noisy, but thresholds are tuned after confirming false positives; the first step is to investigate the drift cause, not silence the signal.
- ✓
Analyze the input data to understand if there is a skew or drift.
Why this is correct
Analysing input data distinguishes training-serving skew from prediction drift, the two distinct axes Vertex AI monitoring reports. Skew arises from preprocessing inconsistencies between training and serving pipelines; drift reflects genuine changes in live data distribution. Inspecting inputs first identifies which mechanism triggered the alert, directing remediation correctly before any retraining or pipeline fix.
- ✗
Retrain the model using the latest data and redeploy.
Why it's wrong here
Retraining immediately skips diagnosis; distribution drift may stem from data quality issues, upstream pipeline changes or seasonal variation, and retraining on drifted data can bake in the problem. It is tempting because retraining addresses drift eventually, but the first step is to investigate the cause via the monitoring console before acting.
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Senior Network & Security Engineer · founder of Courseiva
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