- A
Evaluate the model's bias using a diverse test set across genders
Evaluation is the first step to quantify bias and inform mitigation strategies.
- B
Immediately stop using the model and delete it
Why wrong: Stopping use is drastic; first, the bias should be evaluated and understood.
- C
Fine-tune the model on a gender-balanced dataset
Why wrong: Fine-tuning may help but without evaluation, you cannot confirm the bias or measure improvement.
- D
Add a disclaimer that the model may exhibit bias
Why wrong: Disclaimers do not address the bias; they only inform users.
Question 609 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 data scientist observes that a text generation model consistently produces outputs that stereotype certain genders. According to Google's AI Principles, what is the BEST first step?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
✓
Evaluate the model's bias using a diverse test set across genders
Option A is correct because Google's AI Principles emphasize that the first step in addressing bias is to evaluate and measure it using appropriate tools and diverse datasets. This aligns with Principle #2: 'Avoid creating or reinforcing unfair bias,' which requires testing models across relevant demographic groups before taking corrective action. Without evaluation, any subsequent mitigation steps would lack a baseline and could be ineffective or counterproductive.
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.
- ✓
Evaluate the model's bias using a diverse test set across genders
Why this is correct
Evaluation is the first step to quantify bias and inform mitigation strategies.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Immediately stop using the model and delete it
Why it's wrong here
Stopping use is drastic; first, the bias should be evaluated and understood.
- ✗
Fine-tune the model on a gender-balanced dataset
Why it's wrong here
Fine-tuning may help but without evaluation, you cannot confirm the bias or measure improvement.
- ✗
Add a disclaimer that the model may exhibit bias
Why it's wrong here
Disclaimers do not address the bias; they only inform users.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that mitigation (like fine-tuning or disclaimers) should be the immediate response, rather than the correct first step of systematic evaluation and measurement of bias.
Detailed technical explanation
How to think about this question
Under the hood, bias evaluation involves analyzing model outputs across intersectional subgroups using metrics like demographic parity, equalized odds, or counterfactual fairness. For example, a text generation model might be tested with prompts that vary gender pronouns (e.g., 'The nurse is...' vs. 'The doctor is...') to measure stereotypical associations in generated continuations. In practice, Google's PAIR (People + AI Research) team uses tools like the What-If Tool to probe model behavior across slices of data before any remediation.
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.
- →
Responsible AI and Data Governance — study guide chapter
Learn the concepts, then practise the questions
- →
Responsible AI and Data Governance practice questions
Targeted practice on this topic area only
- →
All Generative AI Leader questions
997 questions across all exam domains
- →
Google Cloud Generative AI Leader Generative AI Leader study guide
Full concept coverage aligned to exam objectives
- →
Generative AI Leader practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related Generative AI Leader practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Fundamentals of Generative AI practice questions
Practise Generative AI Leader questions linked to Fundamentals of Generative AI.
Business Strategies for Generative AI Solutions practice questions
Practise Generative AI Leader questions linked to Business Strategies for Generative AI Solutions.
Generative AI Concepts and Technologies practice questions
Practise Generative AI Leader questions linked to Generative AI Concepts and Technologies.
Google AI Ecosystem and Strategy practice questions
Practise Generative AI Leader questions linked to Google AI Ecosystem and Strategy.
Responsible AI and Data Governance practice questions
Practise Generative AI Leader questions linked to Responsible AI and Data Governance.
Google Cloud's Generative AI Offerings practice questions
Practise Generative AI Leader questions linked to Google Cloud's Generative AI Offerings.
Techniques to Improve Generative AI Model Output practice questions
Practise Generative AI Leader questions linked to Techniques to Improve Generative AI Model Output.
Applying Generative AI in Business practice questions
Practise Generative AI Leader questions linked to Applying Generative AI in Business.
Generative AI Leader fundamentals practice questions
Practise Generative AI Leader questions linked to Generative AI Leader fundamentals.
Generative AI Leader scenario practice questions
Practise Generative AI Leader questions linked to Generative AI Leader scenario.
Generative AI Leader troubleshooting practice questions
Practise Generative AI Leader questions linked to Generative AI Leader troubleshooting.
Practice this exam
Start a free Generative AI Leader practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Evaluate the model's bias using a diverse test set across genders — Option A is correct because Google's AI Principles emphasize that the first step in addressing bias is to evaluate and measure it using appropriate tools and diverse datasets. This aligns with Principle #2: 'Avoid creating or reinforcing unfair bias,' which requires testing models across relevant demographic groups before taking corrective action. Without evaluation, any subsequent mitigation steps would lack a baseline and could be ineffective or counterproductive.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.