AI-900 Practice Question: Describe features of computer vision workloads on Azure
A social media platform wants to automatically review user-uploaded images to flag any that contain explicit or suggestive adult content, as well as violent imagery. Which Azure Computer Vision feature should they use?
⚠ Common exam trap
Many exam-takers confuse 'Image Analysis - Tags' (which describes objects) with content moderation, or assume Face Detection can infer inappropriate content based on facial expressions, but neither performs explicit adult or violence detection.
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
✓
Image Analysis - Moderate content
The 'Moderate content' feature of Azure Computer Vision is specifically designed to detect adult, suggestive, and violent content in images. It returns a binary flag and confidence scores for categories like adult, racy, and gory, making it the appropriate choice for automatically flagging explicit or violent user-uploaded images.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
The Optical Character Recognition (OCR) capability, also known as the Read API, extracts printed or handwritten text from images and converts it to a machine-readable string. It ignores the visual semantic content beyond the text itself, so it cannot detect whether an image contains adult or violent imagery. Even if the text included prohibited words, OCR alone does not provide moderation flags or confidence scores.
When this WOULD be correct
A company needs to extract printed or handwritten text from scanned documents to digitize records. OCR would be the correct feature to use in that scenario.
- ✗
Image Analysis - Tags
Why it's wrong here
The Image Analysis - Tags operation generates a set of descriptive tags for objects, animals, and scenes in an image, such as 'indoor' or 'person'. Unlike Moderate content, it does not produce any confidence scores or labels for adult, racy, or violent themes, so it cannot fulfill the requirement of automatically reviewing and flagging inappropriate content.
When this WOULD be correct
An exam question asks: 'A retail company wants to automatically generate keywords for product images to improve search functionality. Which Azure Computer Vision feature should they use?' In that scenario, Image Analysis - Tags would be correct.
- ✓
Image Analysis - Moderate content
Why this is correct
Image Analysis's Moderate content feature (part of Azure Computer Vision) is specifically designed for content moderation. It returns confidence scores between 0 and 1 for adult, racy, and violent content categories, along with binary flags indicating whether each category is detected. These scores enable automated workflows to flag or block inappropriate images based on custom thresholds, making it the right tool for this use case.
- ✗
Face Detection
Why it's wrong here
Face Detection, typically provided by the Face API, identifies human faces and can extract attributes like age, emotion, and facial hair. It does not assess the broader image for sexual, violent, or adult content, and it is not designed to return moderation confidence scores. Its purpose is biometric analysis, not content safety screening.
When this WOULD be correct
A question asking for a feature to count the number of people in an image or to detect faces for blurring or cropping would make Face Detection 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.
✓Image Analysis - Moderate contentCorrect answer▾
Why this is correct
Image Analysis's Moderate content feature (part of Azure Computer Vision) is specifically designed for content moderation. It returns confidence scores between 0 and 1 for adult, racy, and violent content categories, along with binary flags indicating whether each category is detected. These scores enable automated workflows to flag or block inappropriate images based on custom thresholds, making it the right tool for this use case.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR is used to extract text from images, not to detect explicit or violent content. The question specifically requires content moderation, not text recognition.
★ When this WOULD be the correct answer
A company needs to extract printed or handwritten text from scanned documents to digitize records. OCR would be the correct feature to use in that scenario.
Why candidates choose this
Candidates may confuse OCR with content moderation because both involve analyzing image content, but OCR focuses on text extraction rather than detecting inappropriate material.
✗Image Analysis - TagsWrong answer — click to see why▾
Why this is wrong here
Image Analysis - Tags identifies objects, actions, and concepts in images (e.g., 'beach', 'dog'), but does not specifically detect explicit or violent content. The question requires content moderation, not general tagging.
★ When this WOULD be the correct answer
An exam question asks: 'A retail company wants to automatically generate keywords for product images to improve search functionality. Which Azure Computer Vision feature should they use?' In that scenario, Image Analysis - Tags would be correct.
Why candidates choose this
Candidates may confuse 'tags' with 'moderation tags' or assume that tagging can identify inappropriate content, not realizing that Azure has a dedicated moderation feature for explicit and violent content.
✗Face DetectionWrong answer — click to see why▾
Why this is wrong here
Face Detection identifies and locates human faces in images, but does not analyze content for explicit or violent material, which is the requirement here.
★ When this WOULD be the correct answer
A question asking for a feature to count the number of people in an image or to detect faces for blurring or cropping would make Face Detection the correct answer.
Why candidates choose this
Candidates may mistakenly think that detecting faces is necessary to identify inappropriate content involving people, but moderation is a separate content analysis task.
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
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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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.