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PDE Practice Question: A company deploys a TensorFlow model on Vertex AI…
A company deploys a TensorFlow model on Vertex AI for online predictions. They want to monitor model performance in production to detect degradation. Which TWO practices should they implement? (Choose 2.)
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring for model degradation (data drift/skew) versus monitoring for operational issues (latency, errors), leading candidates to confuse infrastructure alerts with model performance monitoring.
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
✓
Log prediction requests and responses to Cloud Logging and analyze distribution metrics.
Logging prediction requests and responses to Cloud Logging allows you to analyze distribution metrics (e.g., mean, variance, quantiles) over time. This enables detection of data drift or performance degradation by comparing live distributions against baseline distributions, which is a standard monitoring practice for production 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.
- ✗
Use a separate endpoint for shadow testing new model versions.
Why it's wrong here
Shadow testing is a deployment strategy, not a monitoring practice for existing models.
- ✓
Log prediction requests and responses to Cloud Logging and analyze distribution metrics.
Why this is correct
Analyzing request distributions can detect changes in input data patterns that may affect model performance.
- ✗
Set up Cloud Monitoring alerts for high prediction latency.
Why it's wrong here
Latency is a performance metric, not directly related to model accuracy.
- ✗
Schedule daily retraining of the model regardless of monitoring alerts.
Why it's wrong here
Scheduled retraining is not a monitoring practice and may not address root causes.
- ✓
Enable Vertex AI Model Monitoring for feature drift and skew detection on the deployed model.
Why this is correct
Model Monitoring directly detects data drift and skew, which are signs of model degradation.
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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.