- A
Use a pre-trained model without modification
Why wrong: Pre-trained models often inherit biases from their training data.
- B
Remove all demographic information from the resumes before processing
Why wrong: Removing demographic info does not eliminate bias; other signals may proxy for them.
- C
Audit the training data for demographic representativeness and evaluate the model using fairness metrics
Proactive auditing and evaluation help identify and mitigate bias.
- D
Only allow human reviewers to see the top 10% of candidates
Why wrong: This does not address bias in the ranking itself.
Question 507 of 997
Generative AI Leader Responsible AI and Data Governance Practice Question
This Generative AI Leader practice question tests your understanding of responsible ai and data governance. 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 company is using a generative AI model to screen job applications. They want to ensure compliance with Google's AI Principle of avoiding unfair bias. Which practice is most effective in mitigating bias during the screening process?
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
✓
Audit the training data for demographic representativeness and evaluate the model using fairness metrics
Option C is correct because auditing training data for demographic representativeness and evaluating the model using fairness metrics directly addresses the root causes of bias in generative AI systems. This practice aligns with Google's AI Principle of avoiding unfair bias by proactively identifying and mitigating imbalances in the data and measuring the model's performance across demographic groups using metrics like demographic parity or equal opportunity.
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 a pre-trained model without modification
Why it's wrong here
Pre-trained models often inherit biases from their training data.
- ✗
Remove all demographic information from the resumes before processing
Why it's wrong here
Removing demographic info does not eliminate bias; other signals may proxy for them.
- ✓
Audit the training data for demographic representativeness and evaluate the model using fairness metrics
Why this is correct
Proactive auditing and evaluation help identify and mitigate bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Only allow human reviewers to see the top 10% of candidates
Why it's wrong here
This does not address bias in the ranking itself.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that removing demographic features is sufficient to eliminate bias, but the trap here is that proxy variables and latent correlations in the data can still cause the model to discriminate indirectly.
Detailed technical explanation
How to think about this question
Under the hood, fairness metrics such as equalized odds or disparate impact ratio are computed by comparing the model's false positive and false negative rates across demographic groups, requiring access to ground-truth labels and protected attributes in a controlled evaluation set. In practice, a subtle behavior is that even with representative training data, a generative model can exhibit 'representational harm' by generating stereotypical language or associations, which necessitates additional debiasing techniques like adversarial debiasing or counterfactual data augmentation.
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.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
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.
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Responsible AI and Data Governance — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Responsible AI and Data Governance — This question tests Responsible AI and Data Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Audit the training data for demographic representativeness and evaluate the model using fairness metrics — Option C is correct because auditing training data for demographic representativeness and evaluating the model using fairness metrics directly addresses the root causes of bias in generative AI systems. This practice aligns with Google's AI Principle of avoiding unfair bias by proactively identifying and mitigating imbalances in the data and measuring the model's performance across demographic groups using metrics like demographic parity or equal opportunity.
What should I do if I get this Generative AI Leader 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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