- A
Fine-tune the model on a large corpus of legal documents
Why wrong: Fine-tuning improves performance but does not guarantee cited outputs.
- B
Apply a high temperature setting to encourage diverse outputs
Why wrong: High temperature reduces determinism and accuracy.
- C
Use a model larger than 70B parameters
Why wrong: Model size does not guarantee citation; large models can still hallucinate.
- D
Use a RAG architecture that retrieves relevant statutes and includes them as citations in the model's response
RAG with citations provides traceability to specific sources.
Question 897 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 startup is building a generative AI legal document assistant for small law firms. They want to ensure that the model's outputs are accurate and can be traced back to specific legal statutes. Which approach best supports this requirement?
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
✓
Use a RAG architecture that retrieves relevant statutes and includes them as citations in the model's response
Option D is correct because Retrieval-Augmented Generation (RAG) architecture retrieves specific legal statutes from a trusted external knowledge base and includes them as citations in the model's response. This ensures both accuracy (by grounding outputs in verifiable sources) and traceability (by providing direct references to the statutes used). Fine-tuning alone cannot guarantee that the model will cite specific statutes correctly, as it may hallucinate or misremember legal references.
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.
- ✗
Fine-tune the model on a large corpus of legal documents
Why it's wrong here
Fine-tuning improves performance but does not guarantee cited outputs.
- ✗
Apply a high temperature setting to encourage diverse outputs
Why it's wrong here
High temperature reduces determinism and accuracy.
- ✗
Use a model larger than 70B parameters
Why it's wrong here
Model size does not guarantee citation; large models can still hallucinate.
- ✓
Use a RAG architecture that retrieves relevant statutes and includes them as citations in the model's response
Why this is correct
RAG with citations provides traceability to specific sources.
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 misconception that larger models or fine-tuning alone can guarantee factual accuracy and traceability, when in fact retrieval-augmented generation is required for verifiable, source-grounded outputs.
Trap categories for this question
Command / output trap
Fine-tuning improves performance but does not guarantee cited outputs.
Detailed technical explanation
How to think about this question
RAG combines a retrieval component (e.g., using dense passage retrieval with embeddings and a vector database) with a generative model. The retrieval step queries a curated corpus of legal statutes (e.g., from official government databases) and returns the most relevant passages, which are then prepended to the prompt as context. The generative model is instructed to produce answers based solely on the retrieved context, and citations are formatted as inline references (e.g., '42 U.S.C. § 1983'), enabling auditors to verify the source. In practice, this approach also mitigates model drift and ensures compliance with evolving regulations without retraining.
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: Use a RAG architecture that retrieves relevant statutes and includes them as citations in the model's response — Option D is correct because Retrieval-Augmented Generation (RAG) architecture retrieves specific legal statutes from a trusted external knowledge base and includes them as citations in the model's response. This ensures both accuracy (by grounding outputs in verifiable sources) and traceability (by providing direct references to the statutes used). Fine-tuning alone cannot guarantee that the model will cite specific statutes correctly, as it may hallucinate or misremember legal references.
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.
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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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