easyMultiple Choice
PDE Practice Question: A data engineer needs to monitor model…
A data engineer needs to monitor model performance over time for drift detection. What tool is specifically designed for this?
⚠ Common exam trap
Google Cloud often tests the distinction between general-purpose monitoring tools (Cloud Monitoring, Cloud Logging) and ML-specific monitoring services (Vertex AI Model Monitoring), trapping candidates who assume any monitoring tool can handle drift detection.
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 specifically designed to detect prediction drift and feature skew in deployed machine learning models. It continuously analyzes serving data against training data distributions and alerts when statistical metrics (e.g., Jensen-Shannon divergence, L-infinity distance) exceed configured thresholds, making it the correct tool for drift detection in the context of operationalizing ML models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Model Monitoring
Why this is correct
Vertex AI Model Monitoring is purpose-built to track deployed model performance against a training baseline, computing drift and skew metrics on a schedule. It satisfies the drift-detection constraint directly, unlike general logging or dashboards that surface raw metrics without distributional comparison.
- ✗
Cloud Monitoring
Why it's wrong here
Cloud Monitoring tracks infrastructure and application metrics such as CPU, latency and uptime; it does not compute model-specific drift statistics against training distributions. It is tempting because it is the default observability tool in Google Cloud, and it would be correct for alerting on serving latency or error rates rather than feature or prediction drift.
- ✗
Cloud Logging
Why it's wrong here
Cloud Logging captures and stores log entries for querying and retention; it lacks drift metrics, baseline comparisons and automated skew alerts. It is tempting because prediction requests can be logged and inspected manually, but it would be correct for troubleshooting and audit trails, not for systematic drift detection.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML trains and serves models using SQL, but it does not provide ongoing drift detection or performance monitoring over time. It is tempting because it lives beside the training data and can score new rows, yet it would be the right choice for building a model, not for watching one degrade in production.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.