AI-900 Practice Question: Describe features of generative AI workloads on Azure
A social media company uses Azure OpenAI Service to automatically generate captions for user-uploaded images. The company has a strict content policy that prohibits any generated captions containing profanity, hate speech, or self-harm references. Which feature of the Azure OpenAI Service should the company configure to automatically block such harmful content?
⚠ Common exam trap
Test-takers frequently confuse parameters that control output generation (temperature, top-p, max-tokens) with safety mechanisms, assuming any configurable setting can be used to block harmful content, when in fact content filtering is a separate, dedicated feature.
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 filtering
Content filtering is the correct feature because it is specifically designed to detect and block harmful content such as profanity, hate speech, and self-harm references in both input prompts and generated outputs. Azure OpenAI Service's content filtering system uses multi-class classification models to enforce responsible AI policies automatically, without requiring custom training or manual moderation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Temperature parameter
Why it's wrong here
Temperature is a sampling parameter that rescales the token probability distribution, controlling how random or deterministic the model's output is. A higher temperature produces more creative and varied text, while a lower temperature gives more focused and repetitive responses. It has no ability to classify or block harmful content because it operates purely on the next-token distribution, not on semantic safety.
When this WOULD be correct
When the question asks how to control the creativity or variability of generated text (e.g., 'Which parameter should be adjusted to make captions more deterministic?').
- ✗
Top-p parameter
Why it's wrong here
Top-p, or nucleus sampling, sets a cumulative probability threshold so the model only samples from the smallest set of tokens whose combined probability exceeds p. This alters the diversity and coherence of the generated text but never inspects the meaning or potential harm of the tokens. It is a statistical control for output variety, unrelated to content safety filtering.
When this WOULD be correct
A question asking how to control the randomness and diversity of generated text, such as 'Which parameter should be adjusted to make the model's responses more focused and less random?' would make Top-p the correct answer.
- ✓
Content filtering
Why this is correct
Azure OpenAI's content filtering is a dedicated safety layer that scans both prompts and completions for categories such as hate, violence, self-harm, and sexual content. It assigns severity levels and blocks or flags content that exceeds configurable thresholds. This is precisely the feature designed to block prohibited content, making it the correct answer.
- ✗
Max-tokens parameter
Why it's wrong here
Max-tokens limits the maximum length of the generated completion by hard-stopping generation once the token count is reached, effectively truncating the response. It controls the cost and verbosity of the output but performs no semantic evaluation of the content. Consequently, it cannot block prohibited content; it only caps how much text the model can produce.
When this WOULD be correct
A question asking how to limit the number of tokens in a model's response to reduce cost or ensure concise output would have max-tokens as the correct answer.
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.
✓Content filteringCorrect answer▾
Why this is correct
Azure OpenAI's content filtering is a dedicated safety layer that scans both prompts and completions for categories such as hate, violence, self-harm, and sexual content. It assigns severity levels and blocks or flags content that exceeds configurable thresholds. This is precisely the feature designed to block prohibited content, making it the correct answer.
✗Temperature parameterWrong answer — click to see why▾
Why this is wrong here
Temperature controls randomness of output, not content safety; it cannot filter profanity, hate speech, or self-harm references.
★ When this WOULD be the correct answer
When the question asks how to control the creativity or variability of generated text (e.g., 'Which parameter should be adjusted to make captions more deterministic?').
Why candidates choose this
Candidates may confuse parameters that influence output style with safety controls, assuming 'temperature' affects content appropriateness.
✗Top-p parameterWrong answer — click to see why▾
Why this is wrong here
Top-p parameter controls nucleus sampling for output diversity, not content safety; it cannot filter profanity, hate speech, or self-harm references.
★ When this WOULD be the correct answer
A question asking how to control the randomness and diversity of generated text, such as 'Which parameter should be adjusted to make the model's responses more focused and less random?' would make Top-p the correct answer.
Why candidates choose this
Candidates may confuse parameters that influence output quality with safety mechanisms, assuming that adjusting sampling parameters can also block harmful content.
✗Max-tokens parameterWrong answer — click to see why▾
Why this is wrong here
Max-tokens parameter controls the length of generated text, not content safety. It cannot block harmful content like profanity or hate speech.
★ When this WOULD be the correct answer
A question asking how to limit the number of tokens in a model's response to reduce cost or ensure concise output would have max-tokens as the correct answer.
Why candidates choose this
Candidates may mistakenly think that limiting output length (max-tokens) can prevent harmful content, confusing quantity control with content filtering.
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
Responsible AI Principles
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
About these practice questions
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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.