- A
Moderating the resolution and quality of user-uploaded images for platform standards
Why wrong: Image quality checking is technical moderation — image moderation uses AI to detect harmful content categories.
- B
Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores
Image moderation returns adult and racy scores — enabling automatic filtering of inappropriate visual content on platforms.
- C
Modifying images to blur or remove inappropriate elements automatically
Why wrong: Image editing/redaction is a post-moderation action — moderation itself detects and scores content rather than modifying it.
- D
Detecting copyright violations in user-uploaded images by comparing to known copyrighted works
Why wrong: Copyright detection is IP protection — Azure AI Vision's moderation focuses on harmful content categories, not copyright.
Quick Answer
The answer is that Azure AI Vision's image moderation detects sexually explicit (adult) and suggestive (racy) content in images, returning confidence scores for each category. This is correct because the service uses pre-trained computer vision models to analyze visual features and classify content into these two specific categories, rather than performing broader tasks like object detection or copyright checks. On the Microsoft Azure AI Fundamentals AI-900 exam, this tests your understanding of how Azure AI Vision supports content safety and policy compliance, often appearing as a scenario where a social media platform needs to filter inappropriate uploads. A common trap is confusing image moderation with optical character recognition or image captioning—remember it is strictly about adult and racy content detection. Memory tip: think "A-R" for Adult and Racy, the only two categories Azure AI Vision's moderation evaluates.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.
What is 'Azure AI Vision's image moderation' and what content categories does it detect?
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
Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores
Azure AI Vision's image moderation is specifically designed to detect sexually explicit (adult) and suggestive (racy) content in images, returning confidence scores for each category. This is a core feature of the computer vision service that helps platforms comply with content policies by classifying inappropriate visual content rather than modifying images or checking for copyright violations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Moderating the resolution and quality of user-uploaded images for platform standards
Why it's wrong here
Image quality checking is technical moderation — image moderation uses AI to detect harmful content categories.
- ✓
Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores
Why this is correct
Image moderation returns adult and racy scores — enabling automatic filtering of inappropriate visual content on platforms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Modifying images to blur or remove inappropriate elements automatically
Why it's wrong here
Image editing/redaction is a post-moderation action — moderation itself detects and scores content rather than modifying it.
- ✗
Detecting copyright violations in user-uploaded images by comparing to known copyrighted works
Why it's wrong here
Copyright detection is IP protection — Azure AI Vision's moderation focuses on harmful content categories, not copyright.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure AI Vision's image moderation with broader content moderation services (like Azure Content Moderator) or assume it performs automatic actions like blurring, when in fact it only returns classification scores for adult and racy content.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision uses deep neural networks trained on large datasets to classify images into adult and racy categories, outputting a confidence score between 0 and 1 for each. The 'adult' category captures explicit sexual content, while 'racy' includes suggestive or provocative imagery that may not be explicitly explicit. In a real-world scenario, a social media platform could use this moderation to flag user-uploaded photos before publication, but the service returns scores only—the platform must implement its own action (e.g., rejection or manual review) based on thresholds.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores — Azure AI Vision's image moderation is specifically designed to detect sexually explicit (adult) and suggestive (racy) content in images, returning confidence scores for each category. This is a core feature of the computer vision service that helps platforms comply with content policies by classifying inappropriate visual content rather than modifying images or checking for copyright violations.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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