AI-102 Implement generative AI solutions Practice Question
You are implementing a generative AI solution using Azure OpenAI. You need to ensure that the model's outputs do not contain certain inappropriate words or phrases. Which feature should you configure?
⚠ Common exam trap
Microsoft often tests the misconception that system messages (Option A) are sufficient for content safety, when in fact they are only behavioral guidelines and lack the enforcement mechanism of dedicated content filters.
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
✓
Content filters
Content filters in Azure OpenAI are specifically designed to detect and block inappropriate words or phrases in both prompts and completions. They operate at the service level, applying configurable severity thresholds for categories like hate, violence, sexual content, and self-harm, ensuring model outputs adhere to policy without requiring prompt engineering or data modifications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
System message instructions
Why it's wrong here
System messages shape tone, persona and behavioural guardrails, but they cannot guarantee exclusion of specific words because the model may still emit them. Content filtering exists precisely to block named terms and categories at the service layer. System messages suit steering style and task framing, not deterministic term blocking.
- ✗
Grounding with your data
Why it's wrong here
Grounding with your data restricts responses to retrieved source content, improving factual accuracy rather than filtering specific words. It is tempting because it shapes output content, but it is the correct choice for reducing hallucination, not blocking profanity.
- ✓
Content filters
Why this is correct
Content filters in Azure OpenAI evaluate both prompts and completions against configurable severity thresholds across categories such as hate, violence, and sexual content, blocking inappropriate words or phrases. This directly enforces the required output restriction without retraining or prompt engineering.
- ✗
Max tokens limit
Why it's wrong here
A max tokens limit caps response length, truncating output without inspecting or removing any specific words. It is tempting because it constrains generation, but it is the right control when you need to bound cost or response size, not filter content.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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