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PDE Practice Question: You have deployed a classification model on…
You have deployed a classification model on Vertex AI Endpoints. The model's training data had a balanced class distribution, but over time, the production data has shifted such that one class appears 90% of the time. The model's overall accuracy remains high, but the recall for the minority class has dropped significantly. What is the best approach to detect and address this issue?
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring/detection (Model Monitoring) and reactive fixes (threshold tuning), where candidates mistakenly choose a quick fix like adjusting the decision threshold instead of addressing the root cause of data drift.
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
✓
Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data
Vertex AI Model Monitoring is specifically designed to detect skew and drift between training and serving data. In this scenario, the production data has shifted to 90% of one class, which is a clear case of data drift. By setting up monitoring, you can be alerted to this drift and then retrain the model using a sliding window of recent data, which adapts to the new distribution without requiring full retraining on the entire historical dataset. This approach directly addresses the root cause—the shift in class distribution—rather than just treating symptoms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the model daily on the entire historical dataset
Why it's wrong here
Retraining daily on the full historical dataset preserves the original balanced prior, so the model still under-weights the now-dominant class and minority recall stays depressed. It is tempting because frequent retraining sounds responsive, but the fix requires detecting drift and reweighting or resampling recent data, not simply retraining more often.
- ✓
Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data
Why this is correct
Vertex AI Model Monitoring detects training-serving skew and drift against a baseline, surfacing the 90/10 class shift. Retraining on a sliding window of recent data rebalances the learned decision boundary, restoring minority-class recall that aggregate accuracy masks.
- ✗
Increase the number of replicas on the endpoint to reduce latency
Why it's wrong here
Replica count governs serving capacity and latency, not the input distribution, so it cannot detect drift or restore minority-class recall. Scaling replicas is the right response to throughput or latency problems on the endpoint, which is why it appears plausible here despite addressing an unrelated axis.
- ✗
Adjust the decision threshold to improve minority class recall
Why it's wrong here
Lowering the threshold raises minority-class recall but does nothing to detect or correct the underlying covariate shift; it merely trades precision for recall on a model whose learned decision boundary no longer matches production. Threshold tuning suits stable distributions where only the precision-recall balance needs adjusting.
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