AI-900 Practice Question: Describe features of computer vision workloads on Azure
What does Azure AI Vision return when it detects that an image may contain adult content?
⚠ Common exam trap
Watch out — candidates often 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.
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
✓
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.
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
Calling the Analyze Image API produces metadata about the image's content; it does not mutate storage. Azure AI Vision has no way to delete blobs or files—deletion would have to be implemented by your own application using Azure Storage SDK after evaluating the returned scores. Therefore, the API response cannot include an immediate-delete action.
- ✓
Boolean flags and confidence scores for adult, racy, and gory content categories
Why this is correct
For each image, Azure AI Vision returns boolean flags—`isAdultContent`, `isRacyContent`, and `isGoryContent`—along with confidence scores between 0 and 1 that indicate how likely each category is present. These values let the calling application enforce its own moderation thresholds, such as blocking content above 0.8 or routing borderline results for human review. This is the intended output for content moderation decisions.
- ✗
A list of specific body parts detected in the image
Why it's wrong here
The adult content classification is a holistic assessment of the whole scene, not an anatomical detector. Azure AI Vision does not output a list of body parts, nor does it enumerate specific objects related to nudity; doing so would raise privacy concerns and is outside the API's design. Even if tags are generated, they are generic categories and not the structured body-part list described.
- ✗
An age verification requirement for the requesting user
Why it's wrong here
Age verification cannot be performed by Azure AI Vision because the service analyzes pixels in the submitted image and has no access to the requesting user's identity. Knowing whether someone is legally allowed to view adult content requires an authentication mechanism, such as Azure AD/Entra ID or a custom identity provider, at the application layer. The Vision API returns content scores only, and the application is responsible for enforcing any age gate before showing the image.
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
Azure AI Vision
Azure AI Vision is a cloud-based service from Microsoft that uses pre-built machine learning models to extract information from images and videos, such as objects, text, faces, and scene descriptions.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.