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.
An ML team wants to monitor their recommendation model for fairness. Which TWO metrics should they track to detect potential bias? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Pair-wise fairness metrics such as equal opportunity difference.
Pair-wise fairness metrics like equal opportunity difference directly compare model outcomes (e.g., true positive rates) across sensitive groups, making them a standard tool for detecting bias in classification tasks. This metric measures the difference in true positive rates between privileged and unprivileged groups, where a value close to zero indicates fairness. Tracking such metrics aligns with the core principle of monitoring for disparate impact in ML systems.
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.
✓
Pair-wise fairness metrics such as equal opportunity difference.
Prediction distribution (e.g., top-K recommendations) across different sensitive attribute groups.
Why this is correct
Reveals disparities in outcomes.
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 overall accuracy or group-specific recall alone is sufficient for fairness monitoring, when in fact comparative metrics across groups are required to detect bias.
Detailed technical explanation
How to think about this question
Equal opportunity difference is derived from the fairness definition of equal opportunity, which requires that the true positive rate (TPR) be equal across groups. In practice, this metric is computed as TPR_{unprivileged} - TPR_{privileged}, and a threshold (e.g., 0.1) is used to flag potential bias. For recommendation systems, tracking prediction distribution across sensitive groups (Option E) is critical because it reveals whether the model disproportionately recommends certain items (e.g., high-paying jobs) to one demographic, which can lead to systemic discrimination even if per-item accuracy is high.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Pair-wise fairness metrics such as equal opportunity difference. — Pair-wise fairness metrics like equal opportunity difference directly compare model outcomes (e.g., true positive rates) across sensitive groups, making them a standard tool for detecting bias in classification tasks. This metric measures the difference in true positive rates between privileged and unprivileged groups, where a value close to zero indicates fairness. Tracking such metrics aligns with the core principle of monitoring for disparate impact in ML systems.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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