Quick Answer
The answer is hate speech severity detection and self-harm content detection. These two features of Azure AI Content Safety directly address the core challenge of moderating user-generated content in a social media application by providing nuanced, severity-based classification for harmful text and images. Hate speech severity detection categorizes offensive language into low, medium, or high severity levels, allowing moderators to apply context-appropriate actions rather than a simple binary block. Self-harm content detection identifies text or images related to self-injury or suicide, which is critical for enforcing platform safety policies and preventing real-world harm. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of Azure AI Content Safety’s specific content categories—a common trap is confusing these with broader categories like adult content or violence detection. Remember the mnemonic “Hate and Harm” to recall that the two moderation features focused on user-generated content are hate speech severity and self-harm detection.
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.
Which TWO features of Azure AI Content Safety can help you moderate user-generated content in a social media application?
Self-harm content detection.
Detects self-harm content.
Hate speech severity detection.
Detects hate speech content.
PII redaction.
Why wrong: Not a Content Safety feature.
Groundedness detection.
Why wrong: For RAG, not user content.
Prompt injection detection.
Why wrong: Jailbreak detection, not content moderation.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Self-harm content detection.
Self-harm content detection (A) is a feature of Azure AI Content Safety that specifically identifies text or images related to self-harm, which is a critical category for moderating user-generated content in social media to prevent harm and comply with safety policies. Hate speech severity detection (B) is another core feature that classifies hate speech into severity levels (e.g., low, medium, high), enabling nuanced moderation of offensive content.
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.
Self-harm content detection.
Why this is correct
Detects self-harm content.
Related concept
Read the scenario before looking for a memorised answer.
Hate speech severity detection.
Why this is correct
Detects hate speech content.
Related concept
Read the scenario before looking for a memorised answer.
PII redaction.
Why it's wrong here
Not a Content Safety feature.
Groundedness detection.
Why it's wrong here
For RAG, not user content.
Prompt injection detection.
Why it's wrong here
Jailbreak detection, not content moderation.
Common exam traps
The trap here is that candidates may confuse Azure AI Content Safety's features with those of other Azure AI services (like Azure AI Language for PII or Azure OpenAI for prompt injection), leading them to select options that are technically valid in Azure but not part of Content Safety's core moderation capabilities.
Detailed technical explanation
Azure AI Content Safety uses pre-trained models for categories like hate, violence, self-harm, and sexual content, with severity scoring (0-7) to allow fine-grained moderation thresholds. The service can process both text and images via REST APIs, and its severity detection enables social media platforms to apply different actions (e.g., flag, block, or review) based on the severity level, reducing false positives for low-severity content. Under the hood, the models are trained on diverse datasets and support multiple languages, making them suitable for global social media applications.
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: Self-harm content detection. — Self-harm content detection (A) is a feature of Azure AI Content Safety that specifically identifies text or images related to self-harm, which is a critical category for moderating user-generated content in social media to prevent harm and comply with safety policies. Hate speech severity detection (B) is another core feature that classifies hate speech into severity levels (e.g., low, medium, high), enabling nuanced moderation of offensive content.
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 24, 2026
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