- A
Use RAG to retrieve code snippets from a database of known vulnerabilities
Why wrong: RAG with vulnerability data could help identify issues but fine-tuning more directly improves suggestion quality.
- B
Increase the model's temperature parameter to generate more diverse suggestions
Why wrong: Higher temperature increases randomness, which is likely to produce more incorrect suggestions, not fewer.
- C
Switch to a larger foundation model without additional tuning
Why wrong: A larger model may still generate insecure code if not trained on secure code review data.
- D
Fine-tune the base model on a curated dataset of secure code review examples
Fine-tuning on high-quality examples teaches the model to prioritize secure and correct suggestions.
Question 337 of 997
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. 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 piloting a GenAI code review assistant. Developers report that the assistant often suggests incorrect or insecure code snippets. The team wants to improve the assistant's reliability before expanding the pilot. Which approach 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
✓
Fine-tune the base model on a curated dataset of secure code review examples
Fine-tuning on a curated dataset of secure code review examples directly addresses the assistant's tendency to suggest incorrect or insecure code by adapting the model's behavior to the specific patterns and standards of secure coding. Unlike retrieval or parameter adjustments, this approach modifies the model's weights to prioritize security and correctness in its outputs, making it the most effective method for improving reliability in a targeted domain.
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 RAG to retrieve code snippets from a database of known vulnerabilities
Why it's wrong here
RAG with vulnerability data could help identify issues but fine-tuning more directly improves suggestion quality.
- ✗
Increase the model's temperature parameter to generate more diverse suggestions
Why it's wrong here
Higher temperature increases randomness, which is likely to produce more incorrect suggestions, not fewer.
- ✗
Switch to a larger foundation model without additional tuning
Why it's wrong here
A larger model may still generate insecure code if not trained on secure code review data.
- ✓
Fine-tune the base model on a curated dataset of secure code review examples
Why this is correct
Fine-tuning on high-quality examples teaches the model to prioritize secure and correct suggestions.
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 adding more data (via RAG) or increasing model size (larger foundation model) automatically improves output quality, when in fact targeted fine-tuning on domain-specific, high-quality data is required to correct systematic errors in generative outputs.
Detailed technical explanation
How to think about this question
Fine-tuning adjusts the model's weights via supervised learning on a high-quality dataset of secure code reviews, effectively teaching it to prefer patterns like input validation, proper authentication, and safe API usage. Under the hood, this process uses backpropagation to minimize loss on the curated examples, shifting the model's probability distribution away from insecure constructs. In practice, a real-world scenario might involve fine-tuning on a dataset of OWASP Top 10 mitigations to ensure the assistant flags SQL injection or XSS vulnerabilities rather than suggesting them.
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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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fine-tune the base model on a curated dataset of secure code review examples — Fine-tuning on a curated dataset of secure code review examples directly addresses the assistant's tendency to suggest incorrect or insecure code by adapting the model's behavior to the specific patterns and standards of secure coding. Unlike retrieval or parameter adjustments, this approach modifies the model's weights to prioritize security and correctness in its outputs, making it the most effective method for improving reliability in a targeted domain.
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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