The answer is Boolean flags and confidence scores for adult, racy, and gory content categories. Azure AI Vision’s content moderation feature analyzes each image against these three distinct categories, returning a true/false Boolean flag to indicate whether content is detected, along with a confidence score between 0 and 1 that reflects the model’s certainty. On the AI-900 exam, this question tests your understanding of how Azure AI Vision handles adult content detection without modifying or deleting the original image—a key distinction from content filtering services that remove assets. A common trap is assuming it returns a single “adult or not” label, but the service always provides separate Boolean and score outputs for all three categories: adult, racy, and gory. Remember the mnemonic “A-R-G Booleans” to recall Adult, Racy, Gory plus the Boolean and score pair.
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 does Azure AI Vision return when it detects that an image may contain adult content?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Boolean flags and confidence scores for adult, racy, and gory content categories
Azure AI Vision's content moderation feature analyzes images for adult, racy, and gory content. It returns Boolean flags (indicating whether content is detected) and confidence scores (ranging from 0 to 1) for each category, allowing applications to make policy-based decisions without deleting or altering the original image.
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.
✗
The image is immediately deleted from Azure Storage
Why it's wrong here
Azure Vision analyzes and returns scores — it doesn't automatically delete content; that's the application's decision.
✓
Boolean flags and confidence scores for adult, racy, and gory content categories
Why this is correct
Azure Vision returns isAdultContent, isRacyContent, and isGoryContent flags with confidence scores for content moderation decisions.
Related concept
Read the scenario before looking for a memorised answer.
✗
A list of specific body parts detected in the image
Why it's wrong here
Adult content detection returns category flags — not specific anatomical body part identification.
✗
An age verification requirement for the requesting user
Why it's wrong here
Age verification is an application-level identity control — Vision returns content analysis scores for the application to act on.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume Azure AI Vision automatically deletes or blocks content (Option A), when in fact it only returns classification metadata, leaving action decisions to the calling application.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision uses deep neural networks trained on large datasets of adult, racy, and gory images to produce confidence scores between 0 and 1. The 'isAdultContent' Boolean is derived by comparing the adult score against a default threshold (0.4), but developers can adjust this threshold per their content policy. In a real-world scenario, a social media platform might use these scores to flag images for human review rather than automatically blocking them, reducing false positives.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Boolean flags and confidence scores for adult, racy, and gory content categories — Azure AI Vision's content moderation feature analyzes images for adult, racy, and gory content. It returns Boolean flags (indicating whether content is detected) and confidence scores (ranging from 0 to 1) for each category, allowing applications to make policy-based decisions without deleting or altering the original image.
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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