mediumMultiple Choice
PDE Practice Question: Refer to the exhibit
Exhibit
{
"resource": {"type": "ai_platform_endpoint", "labels": {"endpoint_id": "123"}},
"severity": "ERROR",
"jsonPayload": {
"feature_name": "age",
"monitoring_type": "prediction_drift",
"drift_score": 0.85,
"threshold": 0.7
}
}Refer to the exhibit. This log entry was generated by Vertex AI Model Monitoring for a production model. What should the data engineer do to address this issue?
⚠ Common exam trap
Google Cloud often tests the misconception that adjusting thresholds or disabling monitoring is a valid fix for drift, when the correct action is always to retrain the model with current data.
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
✓
Retrain the model with more recent data
Vertex AI Model Monitoring detected a drift in the 'age' feature, indicating that the production data distribution has shifted from the training data. Retraining the model with more recent data aligns the model with the current data distribution, mitigating the drift and maintaining prediction accuracy. This is the standard remediation for model drift in production ML systems.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the drift threshold to 0.9 to suppress alerts
Why it's wrong here
Raising the threshold to 0.9 hides genuine distribution shift rather than resolving it, leaving the model serving predictions on drifted data. Thresholds exist to trigger investigation when drift exceeds tolerance; suppressing alerts is defensible only when the observed drift is confirmed benign and expected.
- ✓
Retrain the model with more recent data
Why this is correct
The monitoring log indicates training-serving skew or drift, where live feature distributions diverge from those the model learned. Retraining on recent production data realigns the model with current patterns, restoring prediction accuracy without altering the serving pipeline.
- ✗
Deploy a new model version trained on the original dataset
Why it's wrong here
Retraining on the original dataset reproduces the same feature distribution the model already learned, so incoming drift persists. Retraining is the right response to concept drift or degraded accuracy, but here it must use recent production data reflecting the shifted distribution, not the stale baseline.
- ✗
Disable monitoring for the 'age' feature
Why it's wrong here
Disabling monitoring for 'age' removes the signal detecting distribution shift, so future drift in that feature goes unnoticed. Feature-level monitoring can legitimately be disabled for features confirmed irrelevant to predictions, but 'age' is typically a meaningful model input, making suppression inappropriate.
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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.