- A
Confidence indicators
Why wrong: Confidence indicators show how sure the model is, but not the reasoning.
- B
Human oversight mechanisms
Why wrong: Human oversight is a requirement but does not provide explainability of the model's output.
- C
Grounding (citing sources)
Why wrong: Grounding provides sources but not the logical steps leading to the diagnosis.
- D
Chain-of-thought reasoning
Chain-of-thought shows the intermediate reasoning steps, enabling clinicians to verify the logic.
Question 836 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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 healthcare organization is using a generative AI model to assist in diagnosing rare diseases from patient symptoms. They want to ensure that model outputs are explainable and that the clinician can verify the reasoning. Which feature should they prioritize?
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
✓
Chain-of-thought reasoning
Chain-of-thought reasoning is the correct feature because it enables the generative AI model to produce an explicit, step-by-step logical path from patient symptoms to a diagnosis. This allows clinicians to verify each reasoning step, ensuring explainability and trust in the model's output, which is critical for rare disease diagnosis where transparency is paramount.
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.
- ✗
Confidence indicators
Why it's wrong here
Confidence indicators show how sure the model is, but not the reasoning.
- ✗
Human oversight mechanisms
Why it's wrong here
Human oversight is a requirement but does not provide explainability of the model's output.
- ✗
Grounding (citing sources)
Why it's wrong here
Grounding provides sources but not the logical steps leading to the diagnosis.
- ✓
Chain-of-thought reasoning
Why this is correct
Chain-of-thought shows the intermediate reasoning steps, enabling clinicians to verify the logic.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between explainability (how the model reached a conclusion) and interpretability (what the model output means), causing candidates to confuse grounding or confidence indicators with the step-by-step reasoning required for clinical verification.
Trap categories for this question
Command / output trap
Confidence indicators show how sure the model is, but not the reasoning.
Detailed technical explanation
How to think about this question
Chain-of-thought reasoning works by prompting the model to generate intermediate natural language steps (e.g., 'Symptom A suggests condition X, but combined with symptom B, it points to Y') before arriving at a final answer. This technique leverages the model's autoregressive nature to produce a traceable inference path, which can be inspected for logical consistency and domain-specific correctness. In practice, for rare diseases, this allows clinicians to spot errors like missing differential diagnoses or incorrect symptom weighting, which a black-box output would obscure.
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: Chain-of-thought reasoning — Chain-of-thought reasoning is the correct feature because it enables the generative AI model to produce an explicit, step-by-step logical path from patient symptoms to a diagnosis. This allows clinicians to verify each reasoning step, ensuring explainability and trust in the model's output, which is critical for rare disease diagnosis where transparency is paramount.
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
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.