This AI-102 practice question tests your understanding of implement generative ai solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
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?
System message instructions
Why wrong: System messages can request avoidance but are not enforced.
Grounding with your data
Why wrong: Grounding ensures answers are based on provided data, not content filtering.
Content filters
Content filters can block inappropriate words and phrases.
Max tokens limit
Why wrong: Max tokens limits length, not content.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 can request avoidance but are not enforced.
Grounding with your data
Why it's wrong here
Grounding ensures answers are based on provided data, not content filtering.
Content filters
Why this is correct
Content filters can block inappropriate words and phrases.
Related concept
Read the scenario before looking for a memorised answer.
Max tokens limit
Why it's wrong here
Max tokens limits length, not content.
Common exam traps
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.
Detailed technical explanation
Azure OpenAI content filters use a multi-level classification system with configurable severity levels (safe, low, medium, high) across four harm categories. They are applied at runtime via the Azure OpenAI Service API, and can be further customized with blocklists for specific terms. In a real-world scenario, a healthcare chatbot might use content filters to block medical misinformation or offensive language, while system messages alone would fail to catch nuanced violations like coded hate speech.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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