- A
Model tuning with citation data
Why wrong: Tuning can help but does not guarantee grounding; explicit grounding mechanisms are needed.
- B
Grounding by referencing source documents in the output
Grounding forces the model to cite sources, increasing trustworthiness.
- C
Chain-of-thought prompting
Why wrong: Chain-of-thought improves reasoning but does not automatically provide citations.
- D
Confidence indicators
Why wrong: Confidence scores do not provide source citations.
Question 110 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 fine-tuned generative model to create marketing copy. They want to ensure that when the model references statistics, it provides citations to original sources. Which technique should they use?
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
✓
Grounding by referencing source documents in the output
Option B is correct because grounding by referencing source documents in the output directly ties the generated statistics to verifiable original sources, ensuring citation accuracy. This technique retrieves and cites specific passages from trusted documents, which is essential for responsible AI in marketing copy where factual claims must be traceable.
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.
- ✗
Model tuning with citation data
Why it's wrong here
Tuning can help but does not guarantee grounding; explicit grounding mechanisms are needed.
- ✓
Grounding by referencing source documents in the output
Why this is correct
Grounding forces the model to cite sources, increasing trustworthiness.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Chain-of-thought prompting
Why it's wrong here
Chain-of-thought improves reasoning but does not automatically provide citations.
- ✗
Confidence indicators
Why it's wrong here
Confidence scores do not provide source citations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that fine-tuning alone can enforce citation behavior, when in fact grounding via retrieval-augmented generation is the standard technique for source-attributed outputs.
Detailed technical explanation
How to think about this question
Grounding typically uses retrieval-augmented generation (RAG), where the model queries a vector database of source documents and appends citations from the retrieved chunks. This approach leverages embedding similarity search (e.g., cosine similarity on sentence transformers) to find the most relevant passages, and the model is prompted to include inline citations like [Source: Document X, Page Y]. In practice, this prevents hallucination for statistical claims but requires careful chunking and metadata management to avoid citing irrelevant or outdated sources.
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.
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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: Grounding by referencing source documents in the output — Option B is correct because grounding by referencing source documents in the output directly ties the generated statistics to verifiable original sources, ensuring citation accuracy. This technique retrieves and cites specific passages from trusted documents, which is essential for responsible AI in marketing copy where factual claims must be traceable.
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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