- A
Eliminate all features that are correlated with protected attributes from the model input to ensure fairness.
Why wrong: Simply removing correlated features is not sufficient and may harm model performance; fairness should be evaluated directly.
- B
Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.
Feature attribution analysis helps identify if the model relies disproportionately on sensitive attributes.
- C
Periodically compare the model's performance metrics (e.g., AUC) on the overall population versus the holdout test set.
Why wrong: Comparing overall performance does not reveal per-group disparities.
- D
Store all model predictions in BigQuery but do not capture ground truth labels to avoid privacy issues.
Why wrong: Without ground truth labels, you cannot compute fairness metrics like equal opportunity or demographic parity.
- E
Set up alerts on the Vertex AI Model Monitoring fairness metrics, such as equal opportunity difference, and configure a slack channel for notifications.
Proactive alerting on fairness metrics is a recommended practice to catch drift in fairness.
Quick Answer
The answer is to set up alerts on Vertex AI Model Monitoring fairness metrics like equal opportunity difference and to configure a Slack channel for notifications. This is correct because fairness monitoring in Vertex AI requires both quantitative detection and immediate response; the platform’s built-in metrics compare model performance across demographic groups, while automated alerts ensure that any drift in fairness—such as disparate false positive rates—triggers real-time action. On the Google Professional Machine Learning Engineer exam, this tests your understanding that monitoring is not just about accuracy but also about operationalizing bias detection through thresholds and notification workflows. A common trap is to focus only on training-time fairness techniques, like reweighting data, while ignoring post-deployment monitoring. Remember the mnemonic “Alert and Act”: set the alert on the fairness metric, then act by routing the notification to a team channel.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. 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 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.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Option B is correct because 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.
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.
- ✗
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Vertex Explainable AI uses techniques like Integrated Gradients or Shapley Value sampling to compute per-feature attribution scores for each prediction. By aggregating these attributions across demographic groups (e.g., using a pivot table in BigQuery), you can identify if features like zip code or income are disproportionately influencing outcomes for certain groups. In practice, this helps detect 'redlining' patterns where the model implicitly uses protected attributes even if they are not directly included as features.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups. — Option B is correct because 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 30, 2026
This PMLE 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 PMLE exam.
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