hardMultiple ChoiceObjective-mapped
PDE Practice Question: A model deployed on Vertex AI Endpoint is making…
A model deployed on Vertex AI Endpoint is making predictions with high accuracy but the business team suspects bias against a certain demographic group. You need to analyze the model's predictions for fairness. What is the most effective approach?
⚠ Common exam trap
Google Cloud often tests the distinction between bias detection (analysis) and bias mitigation (retraining), so candidates may incorrectly choose Option C as a quick fix instead of the correct analytical approach using Explainable AI.
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 AI Explainable AI to generate feature attributions for each prediction and analyze whether the demographic feature has disproportionate impact.
Vertex AI Explainable AI provides per-instance feature attributions, which allow you to examine how the model uses each feature—including sensitive demographic attributes—to arrive at a prediction. By analyzing these attributions across demographic groups, you can detect whether the model disproportionately relies on the demographic feature, indicating potential bias. This approach is more granular than aggregate metrics and directly addresses the business team's concern about bias in individual predictions.
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 Vertex AI Explainable AI to generate feature attributions for each prediction and analyze whether the demographic feature has disproportionate impact.
Why this is correct
Explanations help identify if a sensitive attribute is influencing predictions unfairly.
- ✗
Compute overall fairness metrics by comparing prediction rates across demographic groups.
Why it's wrong here
Fairness metrics require labeled ground truth; predictions alone not sufficient.
- ✗
Collect more data for the under-represented group and retrain the model.
Why it's wrong here
Retraining without diagnosing bias may not address the root cause.
- ✗
Use Vertex AI Model Monitoring to check for training-serving skew on the demographic feature.
Why it's wrong here
Skew detection can hint at data issues but does not directly measure bias in predictions.
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JA
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.