AI-900 Practice Question: Describe features of computer vision workloads on Azure
A home security system uses a camera to detect common household objects such as a person, a pet, a bag, or a package. The system needs to identify the presence and location (bounding box) of these objects in images. The development team wants to use a prebuilt Azure AI service without any custom training. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
It's easy for candidates to confuse Image Captioning (which describes the scene) with Object Detection (which provides precise locations), or assume Custom Vision is needed when the prebuilt Object Detection model already covers the required object categories.
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 – Object Detection
(Image Analysis – Object Detection) is correct because the requirement is to identify both the presence and location (bounding box) of common household objects in images using a prebuilt Azure AI service without custom training. Azure Computer Vision's Image Analysis – Object Detection provides pre-trained models that can detect multiple objects, including people, pets, bags, and packages, and return their bounding box coordinates, exactly matching the scenario.
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
Optical Character Recognition (OCR) is designed to extract printed or handwritten text from images, returning the recognized characters and their location on the page. While an OCR result might include text found on a label or screen, it cannot distinguish or locate a sofa, person, or pet in the scene. The home security scenario needs semantic object categories with bounding boxes, not transcribed text, so OCR is fundamentally the wrong tool.
When this WOULD be correct
A question asks: 'A system needs to extract printed text from scanned documents for digital processing. Which Azure Computer Vision capability should be used?' OCR would be correct.
- ✓
Image Analysis – Object Detection
Why this is correct
The prebuilt Image Analysis Object Detection feature in Azure AI Vision can identify common household objects (e.g., chairs, TVs, pets) and return their bounding-box coordinates along with confidence scores. It requires zero custom training or labeled data, directly satisfying the team's requirement to detect and locate everyday items. This makes it the correct choice for a home security system that needs object awareness out of the box.
- ✗
Image Analysis – Image Captioning
Why it's wrong here
Image Analysis Image Captioning generates a single natural-language sentence summarizing the overall scene, such as 'a living room with a couch and a television.' It does not output bounding boxes or per-object coordinates, so it cannot tell the system where each detected object is positioned. Even though the caption may mention correct objects, the lack of spatial localization makes it unsuitable for a security system that must track specific items.
When this WOULD be correct
A question asking for generating a natural language description of an image's content, such as 'Describe what is happening in this photo' for accessibility or content moderation, where no object localization is needed.
- ✗
Custom Vision
Why it's wrong here
Custom Vision is a separate Azure service for building bespoke image classifiers or object detectors using your own labeled dataset, requiring model training and iteration. It is not a prebuilt capability, so it contradicts the team's explicit desire to avoid custom training. For standard household objects, the generic Image Analysis Object Detection model is already trained on a broad ontology, making Custom Vision unnecessary and overkill for this requirement.
When this WOULD be correct
A scenario where the home security system needs to detect unique objects not covered by prebuilt models, such as specific brand packages or rare pet breeds, and the team is willing to provide labeled training images.
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 – Object DetectionCorrect answer▾
Why this is correct
The prebuilt Image Analysis Object Detection feature in Azure AI Vision can identify common household objects (e.g., chairs, TVs, pets) and return their bounding-box coordinates along with confidence scores. It requires zero custom training or labeled data, directly satisfying the team's requirement to detect and locate everyday items. This makes it the correct choice for a home security system that needs object awareness out of the box.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
Optical Character Recognition (OCR) extracts text from images, not objects like people or pets. The requirement is to detect objects and their bounding boxes, not text.
★ When this WOULD be the correct answer
A question asks: 'A system needs to extract printed text from scanned documents for digital processing. Which Azure Computer Vision capability should be used?' OCR would be correct.
Why candidates choose this
Candidates may confuse OCR with general image analysis or think that detecting text on packages (e.g., labels) is the primary need, overlooking the object detection requirement.
✗Image Analysis – Image CaptioningWrong answer — click to see why▾
Why this is wrong here
Image Captioning generates a textual description of the entire image, but does not identify specific objects with bounding boxes, which is required for detecting presence and location of objects like a person or package.
★ When this WOULD be the correct answer
A question asking for generating a natural language description of an image's content, such as 'Describe what is happening in this photo' for accessibility or content moderation, where no object localization is needed.
Why candidates choose this
Candidates may confuse 'detecting objects' with 'describing objects' and assume captioning can provide object presence, not realizing it lacks spatial localization (bounding boxes).
✗Custom VisionWrong answer — click to see why▾
Why this is wrong here
Custom Vision requires custom training with labeled images, but the question specifies using a prebuilt Azure AI service without any custom training.
★ When this WOULD be the correct answer
A scenario where the home security system needs to detect unique objects not covered by prebuilt models, such as specific brand packages or rare pet breeds, and the team is willing to provide labeled training images.
Why candidates choose this
Candidates may think Custom Vision is the only way to detect objects, not realizing that prebuilt object detection in Image Analysis already covers common household objects like people, pets, bags, and packages.
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
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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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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.