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PMLE Practice Question: A financial services company has deployed a…
A financial services company has deployed a credit risk ML model on Vertex AI. They want to monitor the model for fairness across demographic groups to ensure no biased outcomes. Which TWO actions should they take as best practices? (Choose TWO.)
⚠ Common exam trap
Google Cloud often tests the misconception that removing protected attributes or correlated features is sufficient for fairness, when in reality proxy features and complex interactions can still cause bias, making monitoring with explainability and fairness metrics essential.
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
✓
Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.
Vertex Explainable AI provides feature attribution scores that can be compared across demographic groups to detect if the model relies on sensitive attributes or proxies. This enables fairness auditing by revealing whether the model's decision logic differs systematically for protected groups, which is a best practice for monitoring bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Eliminate all features that are correlated with protected attributes from the model input to ensure fairness.
Why it's wrong here
Simply removing correlated features is not sufficient and may harm model performance; fairness should be evaluated directly.
- ✓
Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.
Why this is correct
Feature attribution analysis helps identify if the model relies disproportionately on sensitive attributes.
- ✗
Periodically compare the model's performance metrics (e.g., AUC) on the overall population versus the holdout test set.
Why it's wrong here
Comparing overall performance does not reveal per-group disparities.
- ✗
Store all model predictions in BigQuery but do not capture ground truth labels to avoid privacy issues.
Why it's wrong here
Without ground truth labels, you cannot compute fairness metrics like equal opportunity or demographic parity.
- ✓
Set up alerts on the Vertex AI Model Monitoring fairness metrics, such as equal opportunity difference, and configure a slack channel for notifications.
Why this is correct
Proactive alerting on fairness metrics is a recommended practice to catch drift in fairness.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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