AI-900 Practice Question: Describe features of generative AI workloads on Azure
A social media platform uses Azure OpenAI Service to generate summaries of user comments. The development team discovers that sometimes the generated summaries include offensive or harmful language that was present in the original comments. The team wants to ensure that the generated output is always free of hate speech, profanity, and self-harm references. What should the team configure in the Azure OpenAI Service?
⚠ Common exam trap
It's easy for candidates to confuse model parameters like temperature or max_tokens with safety controls, or assume that grounding sources automatically sanitize output, when in fact content filters are the dedicated mechanism for blocking harmful language.
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
✓
Configure a content filter
Azure OpenAI Service provides built-in content filtering that can be configured to block hate speech, profanity, and self-harm references in both input prompts and generated completions. This ensures that even if offensive language appears in the original user comments, the generated summaries will be free of such harmful content. The content filter operates at the service level, applying predefined severity thresholds to filter out undesirable language.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the temperature parameter to 0
Why it's wrong here
Temperature is a sampling parameter that controls the randomness of token selection, with 0 forcing greedy deterministic decoding. Lowering temperature makes outputs more reproducible but has no effect on whether the model recognizes or blocks harmful content. An unsafe prompt can generate an equally unsafe answer even at temperature 0, because content filtering is a separate safety mechanism.
When this WOULD be correct
When the question asks for reducing creativity or variability in generated text, such as ensuring consistent factual responses in a customer service chatbot, setting temperature to 0 would be correct.
- ✓
Configure a content filter
Why this is correct
Content filters in Azure OpenAI Service allow you to define blocklists for categories like hate, sexual, violence, and self-harm, with severity levels. This filter is applied to both the user prompt and the model completion, so any harmful content is blocked before generation. Configuring a content filter directly satisfies the requirement to ensure the service does not process or return unsafe content.
- ✗
Increase the max_tokens parameter
Why it's wrong here
The max_tokens parameter controls the maximum number of tokens the model can generate in a single completion. It does not inspect or modify the content of the input prompt, nor does it classify harmful material. Increasing this value only permits longer outputs, which could even produce more unsafe text if the prompt is malicious, so it cannot address the requirement.
When this WOULD be correct
In a scenario where the generated summaries are being cut off prematurely and losing important information, increasing max_tokens would allow the model to produce longer, more complete summaries.
- ✗
Use a grounding source
Why it's wrong here
A grounding source, such as Azure Cognitive Search or uploaded custom data, provides additional context that helps the model stay factual and reduce hallucinations. Grounding does not intercept or evaluate the user's prompt for harmful categories, and a malicious prompt can still be processed. It focuses on improving answer relevance, not on filtering content toxicity, so it is not the correct solution.
When this WOULD be correct
A question where the model generates incorrect or fabricated information (hallucinations) and needs to be anchored to verified data, e.g., 'An enterprise chatbot must answer customer queries based only on the company's official documentation. What should be configured?'
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Configure a content filterCorrect answer▾
Why this is correct
Content filters in Azure OpenAI Service allow you to define blocklists for categories like hate, sexual, violence, and self-harm, with severity levels. This filter is applied to both the user prompt and the model completion, so any harmful content is blocked before generation. Configuring a content filter directly satisfies the requirement to ensure the service does not process or return unsafe content.
✗Set the temperature parameter to 0Wrong answer — click to see why▾
Why this is wrong here
Setting the temperature parameter to 0 makes the model deterministic but does not filter offensive content; it only reduces randomness in output.
★ When this WOULD be the correct answer
When the question asks for reducing creativity or variability in generated text, such as ensuring consistent factual responses in a customer service chatbot, setting temperature to 0 would be correct.
Why candidates choose this
Candidates may think that lowering temperature eliminates undesirable outputs, but it only affects randomness, not content safety.
✗Increase the max_tokens parameterWrong answer — click to see why▾
Why this is wrong here
Increasing max_tokens only extends the length of generated summaries, but does not filter or remove offensive content. It does not address the requirement to eliminate hate speech, profanity, or self-harm references.
★ When this WOULD be the correct answer
In a scenario where the generated summaries are being cut off prematurely and losing important information, increasing max_tokens would allow the model to produce longer, more complete summaries.
Why candidates choose this
Candidates may think that by allowing more tokens, the model will have more context to avoid generating harmful content, but token limits do not affect content safety filtering.
✗Use a grounding sourceWrong answer — click to see why▾
Why this is wrong here
Using a grounding source helps improve factual accuracy by linking to external data, but it does not filter offensive language. The question specifically requires removing hate speech, profanity, and self-harm references, which is done via content filters.
★ When this WOULD be the correct answer
A question where the model generates incorrect or fabricated information (hallucinations) and needs to be anchored to verified data, e.g., 'An enterprise chatbot must answer customer queries based only on the company's official documentation. What should be configured?'
Why candidates choose this
Candidates may confuse 'grounding' with content filtering, thinking that providing a source of acceptable content will automatically exclude harmful language, but grounding does not perform explicit moderation.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Content filter
A content filter is an AI-powered safety system that screens user prompts and AI-generated responses for harmful, offensive, or restricted content, helping to ensure responsible use of Azure AI services.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.