- A
Increase top-k sampling to generate a wider variety of code tokens.
Why wrong: Top-k does not address security.
- B
After each generation, automatically run the code through the static analysis scanner, and if vulnerabilities are found, send the output back to the model for revision with the scanner's feedback.
This iterative process catches and corrects security issues without manual intervention, keeping velocity high.
- C
Fine-tune the model on a corpus of secure code examples.
Why wrong: Fine-tuning reduces but does not guarantee elimination of all vulnerabilities.
- D
Add a system prompt: 'Do not generate code with security vulnerabilities.'
Why wrong: Prompt instructions are not enforceable; the model may still generate unsafe code.
Question 684 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 software development team builds an internal code assistant using a generative model. The assistant writes Python functions that often contain security vulnerabilities such as SQL injection or command injection. The team wants to mitigate these vulnerabilities without adding a manual review step for every code snippet, as that would slow development. They have access to a static analysis security scanner API. Which approach best addresses the vulnerabilities while maintaining developer velocity?
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
✓
After each generation, automatically run the code through the static analysis scanner, and if vulnerabilities are found, send the output back to the model for revision with the scanner's feedback.
Option B is correct because it creates an automated feedback loop: the static analysis scanner detects vulnerabilities in the generated code, and the model revises the output based on that feedback. This approach directly mitigates security flaws without requiring manual review, preserving developer velocity. It leverages the scanner's precise, rule-based detection to iteratively improve the model's output, which is more reliable than relying on the model's inherent safety.
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.
- ✗
Increase top-k sampling to generate a wider variety of code tokens.
Why it's wrong here
Top-k does not address security.
- ✓
After each generation, automatically run the code through the static analysis scanner, and if vulnerabilities are found, send the output back to the model for revision with the scanner's feedback.
Why this is correct
This iterative process catches and corrects security issues without manual intervention, keeping velocity high.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tune the model on a corpus of secure code examples.
Why it's wrong here
Fine-tuning reduces but does not guarantee elimination of all vulnerabilities.
- ✗
Add a system prompt: 'Do not generate code with security vulnerabilities.'
Why it's wrong here
Prompt instructions are not enforceable; the model may still generate unsafe code.
Common exam traps
Common exam trap: answer the scenario, not the keyword
This exam often tests the misconception that a simple prompt or fine-tuning alone can guarantee safety, when in reality, a closed-loop validation with a dedicated security tool is required for reliable mitigation of injection vulnerabilities.
Detailed technical explanation
How to think about this question
Static analysis scanners (e.g., Bandit for Python) use abstract syntax tree (AST) parsing and pattern matching to detect known vulnerability signatures like raw SQL string concatenation or use of os.system(). The iterative feedback loop in option B effectively performs a form of rejection sampling: the model generates a candidate, the scanner evaluates it, and if a vulnerability is flagged, the model receives the scanner's output (e.g., line numbers and CWE identifiers) as context for a targeted revision. This approach is similar to 'self-critique' or 'reflexion' patterns in LLM agents, where the model uses external tool feedback to refine its own output.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: After each generation, automatically run the code through the static analysis scanner, and if vulnerabilities are found, send the output back to the model for revision with the scanner's feedback. — Option B is correct because it creates an automated feedback loop: the static analysis scanner detects vulnerabilities in the generated code, and the model revises the output based on that feedback. This approach directly mitigates security flaws without requiring manual review, preserving developer velocity. It leverages the scanner's precise, rule-based detection to iteratively improve the model's output, which is more reliable than relying on the model's inherent safety.
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
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