The correct choice is to enable Azure AI Content Safety with a custom blocklist for harmful code. This measure is essential because system messages alone cannot reliably prevent harmful code generation in Azure OpenAI outputs; the model may still interpret instructions loosely or be manipulated by adversarial prompts. Azure AI Content Safety acts as a dedicated content filtering layer at the inference level, and a custom blocklist lets you define specific patterns—such as malware code snippets or dangerous function calls—that the model is prohibited from outputting, enforcing safety beyond the system message. On the AI-102 exam, this scenario tests your understanding of defense-in-depth for generative AI, where prompt engineering is a first line but not a complete solution. A common trap is assuming a stronger system message alone will suffice, but the exam emphasizes that Azure AI Content Safety is the service-level control for blocking prohibited content. Memory tip: “System messages guide, but blocklists decide—Content Safety is the final gate.”
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. 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.
Exhibit
{
"role": "system",
"content": "You are an AI assistant that generates code. When asked to write code, always include comments explaining the code. If the user asks for something harmful, refuse and suggest an alternative."
}
Refer to the exhibit. You are configuring a system message for an Azure OpenAI deployment. The assistant is still generating harmful code despite the instruction. Which additional measure should you implement?
{
"role": "system",
"content": "You are an AI assistant that generates code. When asked to write code, always include comments explaining the code. If the user asks for something harmful, refuse and suggest an alternative."
}
A
Fine-tune the model on safe code examples.
Why wrong: May not fully prevent harmful code.
B
Lower the temperature parameter to 0.
Why wrong: Temperature does not enforce safety.
C
Add more examples to the prompt.
Why wrong: Examples may not be sufficient.
D
Enable Azure AI Content Safety with a custom blocklist for harmful code.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Azure AI Content Safety with a custom blocklist for harmful code.
Option D is correct because Azure AI Content Safety provides a dedicated content filtering layer that can block harmful code generation at the inference level, regardless of the system message. A custom blocklist allows you to define specific patterns (e.g., code snippets for malware) that the model is prohibited from outputting, enforcing safety beyond prompt instructions.
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 safe code examples.
Why it's wrong here
May not fully prevent harmful code.
✗
Lower the temperature parameter to 0.
Why it's wrong here
Temperature does not enforce safety.
✗
Add more examples to the prompt.
Why it's wrong here
Examples may not be sufficient.
✓
Enable Azure AI Content Safety with a custom blocklist for harmful code.
Why this is correct
Content filtering can block harmful content.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume prompt engineering (system messages or few-shot examples) is sufficient for safety, but Azure OpenAI requires explicit content filtering via Azure AI Content Safety to reliably block harmful outputs at scale.
Detailed technical explanation
How to think about this question
Azure AI Content Safety operates as a post-processing filter that evaluates model outputs against configurable severity levels and blocklists, using natural language processing to detect harmful content such as code that executes system commands or accesses sensitive data. The custom blocklist feature allows administrators to define regex patterns or exact strings (e.g., 'rm -rf /' or 'Invoke-Mimikatz') that trigger automatic blocking, providing a defense-in-depth layer that complements system messages. In a real-world scenario, this is critical for enterprise deployments where a single harmful output could lead to security breaches or compliance violations.
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.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable Azure AI Content Safety with a custom blocklist for harmful code. — Option D is correct because Azure AI Content Safety provides a dedicated content filtering layer that can block harmful code generation at the inference level, regardless of the system message. A custom blocklist allows you to define specific patterns (e.g., code snippets for malware) that the model is prohibited from outputting, enforcing safety beyond prompt instructions.
What should I do if I get this AI-102 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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Question Discussion
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