hardMultiple ChoiceObjective-mapped
PDE Practice Question: A data science team deploys a TensorFlow image…
A data science team deploys a TensorFlow image classification model to Vertex AI Prediction. The model performs well in offline evaluation but shows a 15% drop in accuracy in production. The production data distribution has shifted compared to the training data. The team needs to continuously monitor and retrain the model. Which solution is most appropriate for detecting drift and triggering retraining?
⚠ Common exam trap
Google Cloud often tests the distinction between operational monitoring (latency, errors) and data-quality monitoring (feature drift), leading candidates to mistakenly choose Cloud Monitoring (Option D) because they confuse production health metrics with model-specific distribution shifts.
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 Vertex AI Model Monitoring for feature drift; configure alerts to trigger a Vertex AI Pipelines retraining run.
Vertex AI Model Monitoring is purpose-built for detecting feature drift in production ML models by comparing live inference data against a baseline distribution. When drift is detected, it can directly trigger a Vertex AI Pipelines retraining run, creating an automated, end-to-end MLOps loop that addresses the production accuracy drop without manual intervention.
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 Vertex AI Model Monitoring for feature drift; configure alerts to trigger a Vertex AI Pipelines retraining run.
Why this is correct
Vertex AI Model Monitoring detects drift and can trigger automated retraining.
- ✗
Export production predictions to Cloud Logging, then use Log Analytics to compare distributions.
Why it's wrong here
Logging is not designed for distribution analysis.
- ✗
Store predictions in BigQuery and run scheduled SQL queries to detect drift; trigger retraining via Cloud Functions.
Why it's wrong here
BigQuery is a storage/query service, not a drift detection tool.
- ✗
Use Cloud Monitoring to track prediction latency and error rates; manually retrain when errors increase.
Why it's wrong here
Monitoring latency/errors does not detect data drift.
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