The answer is Image Analysis – Object Detection. This is the correct choice because Azure Computer Vision’s prebuilt object detection capability is specifically designed to identify common household objects—like a person, pet, bag, or package—and return their precise bounding box coordinates, all without any custom training. The service uses pre-trained models that match the exact scenario of a home security system needing both presence and location data. On the AI-900 exam, this question tests your understanding of which prebuilt Azure AI service to select when the requirement includes spatial localization, not just classification. A common trap is confusing Optical Character Recognition (OCR) or a custom Vision solution, but the key differentiator here is the need for bounding boxes on everyday objects. Remember the memory tip: if you need to know *where* an object is, think “Object Detection” for the box; if you only need to know *what* it is, think “Image Classification” for the label.
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 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?
Why wrong: OCR is for extracting printed or handwritten text from images, not for detecting objects like people or pets.
B
Image Analysis – Object Detection
This prebuilt feature can detect common objects and provide bounding box coordinates without any custom training. It fits the requirement to identify and locate household objects.
C
Image Analysis – Image Captioning
Why wrong: Image captioning generates a sentence describing the image content but does not give bounding boxes or precise object locations.
D
Custom Vision
Why wrong: Custom Vision requires training a model with labeled images, which is not 'prebuilt' and would need custom training. The team wants to avoid custom training.
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
Option B (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.
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 is for extracting printed or handwritten text from images, not for detecting objects like people or pets.
✓
Image Analysis – Object Detection
Why this is correct
This prebuilt feature can detect common objects and provide bounding box coordinates without any custom training. It fits the requirement to identify and locate household objects.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image Analysis – Image Captioning
Why it's wrong here
Image captioning generates a sentence describing the image content but does not give bounding boxes or precise object locations.
✗
Custom Vision
Why it's wrong here
Custom Vision requires training a model with labeled images, which is not 'prebuilt' and would need custom training. The team wants to avoid custom training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Azure Computer Vision's Object Detection uses deep neural networks trained on the COCO dataset, which includes 80 common object categories such as person, dog, cat, backpack, and handbag. The API returns a JSON response with an array of detected objects, each containing a bounding box defined by x, y, width, and height coordinates relative to the image dimensions, along with a confidence score. This capability is ideal for real-time scenarios like home security, where detecting and localizing multiple object types in a single frame is critical.
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: Image Analysis – Object Detection — Option B (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.
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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