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.
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?
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
Option C is correct because 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.
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.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, not content moderation for adult or violent imagery.
✗
Image Analysis - Tags
Why it's wrong here
Tags describe objects, animals, and scenes in an image but do not classify content for adult or violent themes.
✓
Image Analysis - Moderate content
Why this is correct
This feature returns confidence scores for adult, racy, and violent content categories, enabling automatic flagging of inappropriate images.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face Detection
Why it's wrong here
Face Detection finds human faces and attributes (e.g., age, emotion) but does not assess content for adult or violent material.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
The 'Moderate content' feature uses a classification model trained on thousands of images to assign confidence scores (0 to 1) for adult, racy, and gory categories. It also supports a binary 'isAdultContent' flag for strict filtering. In a real-world scenario, a social media platform might combine this with a human review queue for borderline cases, as the API's confidence thresholds can be adjusted to balance false positives and false negatives.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Image Analysis - Moderate content — Option C is correct because 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.
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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Question Discussion
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