- A
Include a system prompt that instructs the model to never generate code using internal APIs.
Why wrong: Prompt instructions are not reliably followed, especially under adversarial inputs.
- B
Use a retriever to fetch policy documents and prepend them to each prompt.
Why wrong: RAG does not guarantee the model will adhere to policy during generation.
- C
Fine-tune the model on a dataset of code snippets that follow access control policies, including negative examples of disallowed API usage.
Fine-tuning directly embeds the policy into model weights.
- D
Train a separate classifier to rerank model outputs and reject non-compliant generations.
Why wrong: A classifier can filter outputs but is less integrated than fine-tuning; it may miss subtle violations.
Question 777 of 997
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. 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 company is developing a code generation assistant and wants to ensure the model respects access control policies, e.g., it should not generate code that uses internal APIs that the user is not authorized to access. Which technique is most effective for embedding such policy constraints into the model's behavior?
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 model on a dataset of code snippets that follow access control policies, including negative examples of disallowed API usage.
Fine-tuning on a curated dataset that includes both positive examples (compliant code) and negative examples (disallowed API usage) directly adjusts the model's weights to internalize access control policies. This method is more effective than prompt-based approaches because it modifies the model's underlying behavior rather than relying on fragile instructions that can be ignored or overridden, especially in complex code generation tasks.
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.
- ✗
Include a system prompt that instructs the model to never generate code using internal APIs.
Why it's wrong here
Prompt instructions are not reliably followed, especially under adversarial inputs.
- ✗
Use a retriever to fetch policy documents and prepend them to each prompt.
Why it's wrong here
RAG does not guarantee the model will adhere to policy during generation.
- ✓
Fine-tune the model on a dataset of code snippets that follow access control policies, including negative examples of disallowed API usage.
Why this is correct
Fine-tuning directly embeds the policy into model weights.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Train a separate classifier to rerank model outputs and reject non-compliant generations.
Why it's wrong here
A classifier can filter outputs but is less integrated than fine-tuning; it may miss subtle violations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google Gen AI Leader exam often tests the misconception that prompt engineering or retrieval-augmented generation (RAG) can reliably enforce complex, context-dependent policies, when in fact fine-tuning is required to deeply integrate constraints into the model's behavior.
Trap categories for this question
Command / output trap
A classifier can filter outputs but is less integrated than fine-tuning; it may miss subtle violations.
Detailed technical explanation
How to think about this question
Fine-tuning for policy compliance uses supervised learning on a dataset where each code snippet is labeled with access control annotations, often employing techniques like instruction tuning or RLHF with a reward model that penalizes disallowed API calls. Under the hood, this adjusts the transformer's attention weights and token probabilities so that internal API calls are deprioritized for unauthorized users, effectively embedding the policy into the model's parametric knowledge. In real-world deployments, this approach is combined with runtime verification (e.g., static analysis) to catch edge cases, but fine-tuning remains the primary method for reducing policy violations at scale.
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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fine-tune the model on a dataset of code snippets that follow access control policies, including negative examples of disallowed API usage. — Fine-tuning on a curated dataset that includes both positive examples (compliant code) and negative examples (disallowed API usage) directly adjusts the model's weights to internalize access control policies. This method is more effective than prompt-based approaches because it modifies the model's underlying behavior rather than relying on fragile instructions that can be ignored or overridden, especially in complex code generation tasks.
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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