easyMultiple Choice
PMLE Practice Question: A data science team has deployed a model on…
A data science team has deployed a model on Vertex AI and wants to automatically detect when the distribution of a specific feature shifts significantly from the training data. Which service should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring model performance (e.g., accuracy, latency) versus monitoring data distribution drift, and candidates may confuse Vertex AI Model Monitoring with Explainable AI because both involve model analysis, but only Model Monitoring tracks shifts over time.
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
✓
Vertex AI Model Monitoring
Vertex AI Model Monitoring is the correct service because it is specifically designed to detect feature distribution drift (skew) between training and serving data for deployed models. It continuously monitors the input features and alerts when statistical metrics like the Jensen-Shannon divergence or the L-infinity distance exceed a configured threshold, enabling proactive model retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Data Loss Prevention
Why it's wrong here
Cloud DLP discovers and redacts sensitive data such as personally identifiable information; it performs no statistical comparison of feature distributions. It is tempting because it inspects data content, which would be correct for classifying or de-identifying sensitive records, not for detecting training-serving drift.
- ✓
Vertex AI Model Monitoring
Why this is correct
Vertex AI Model Monitoring computes feature distribution statistics on live traffic and compares them with the training baseline, automatically flagging significant shifts. This satisfies the requirement to detect distribution changes for a specific feature without building custom drift-detection pipelines.
- ✗
Vertex AI Explainable AI
Why it's wrong here
Vertex AI Explainable AI attributes individual predictions to input features via methods such as Shapley values; it does not monitor live feature distributions against training baselines. It is tempting because it analyses model inputs, which would be correct for interpreting why a prediction was made, not for drift detection.
- ✗
Cloud Composer
Why it's wrong here
Cloud Composer orchestrates workflows via Airflow DAGs; it schedules pipelines but computes no distribution statistics or drift thresholds. It is tempting because it can run scheduled jobs, which would be correct for coordinating recurring batch pipelines, not for detecting feature drift on a deployed Vertex AI model.
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