- A
Use Vertex AI Explainable AI to generate feature attributions for each prediction and analyze whether the demographic feature has disproportionate impact.
Explanations help identify if a sensitive attribute is influencing predictions unfairly.
- B
Compute overall fairness metrics by comparing prediction rates across demographic groups.
Why wrong: Fairness metrics require labeled ground truth; predictions alone not sufficient.
- C
Collect more data for the under-represented group and retrain the model.
Why wrong: Retraining without diagnosing bias may not address the root cause.
- D
Use Vertex AI Model Monitoring to check for training-serving skew on the demographic feature.
Why wrong: Skew detection can hint at data issues but does not directly measure bias in predictions.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Vertex AI Explainable AI uses techniques like Integrated Gradients or Shapley value approximations to compute feature importance scores for each prediction. By aggregating these attributions across demographic groups (e.g., comparing the average attribution for the demographic feature between groups), you can quantify whether the model is using that feature as a proxy for bias. In practice, this method is critical for regulatory compliance (e.g., GDPR, CCPA) where explainability is required for high-stakes decisions like loan approvals or hiring.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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