- A
Optical Character Recognition (OCR)
Why wrong: OCR is used to extract printed or handwritten text from images. It does not detect objects like furniture.
- B
Image Analysis (prebuilt)
Why wrong: Prebuilt Image Analysis can generate tags and descriptions of an image but does not return object bounding boxes. It may identify a bed as a tag, but it cannot precisely locate or confirm the presence of multiple specific objects in the way object detection does.
- C
Object Detection
Azure Computer Vision's prebuilt object detection identifies common objects (such as bed, desk, chair) in an image and returns their locations with bounding boxes. This is the correct capability for verifying the presence of specific furniture items.
- D
Handwriting OCR
Why wrong: Handwriting OCR is a specialized feature for reading handwritten text. It is unrelated to detecting objects in images.
Quick Answer
The answer is Object Detection, because this prebuilt Azure AI Vision feature can identify and locate multiple specific amenities like a bed, desk, and chair within hotel photos by drawing bounding boxes around each object. Unlike image classification, which only labels the entire scene, object detection pinpoints exactly where each amenity is, making it ideal for verifying that all required items are present in a room. On the AI-900 exam, this scenario tests your understanding of when to use prebuilt vision capabilities versus custom training—a common trap is confusing object detection with image classification or optical character recognition. Remember that if the task requires finding *where* things are in an image, not just *what* is there, object detection is your go-to. A helpful memory tip: think "detect and locate" for object detection, versus "classify the whole picture" for image classification.
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 hotel booking website wants to automatically analyze guest-submitted photos of hotel rooms to verify if they contain common amenities such as a bed, a desk, and a chair. They want to use a prebuilt Azure AI service without any custom training. Which feature should they use?
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
Object Detection
Object Detection (prebuilt) is the correct choice because it can identify and locate multiple specific objects (bed, desk, chair) within an image by drawing bounding boxes around them. This prebuilt Azure AI Vision feature requires no custom training and directly supports detecting common amenities in hotel room photos.
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 used to extract printed or handwritten text from images. It does not detect objects like furniture.
- ✗
Image Analysis (prebuilt)
Why it's wrong here
Prebuilt Image Analysis can generate tags and descriptions of an image but does not return object bounding boxes. It may identify a bed as a tag, but it cannot precisely locate or confirm the presence of multiple specific objects in the way object detection does.
- ✓
Object Detection
Why this is correct
Azure Computer Vision's prebuilt object detection identifies common objects (such as bed, desk, chair) in an image and returns their locations with bounding boxes. This is the correct capability for verifying the presence of specific furniture items.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Handwriting OCR
Why it's wrong here
Handwriting OCR is a specialized feature for reading handwritten text. It is unrelated to detecting objects in images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'Image Analysis' (which provides descriptive tags but not precise object localization) with 'Object Detection' (which provides bounding boxes for specific objects), leading them to choose Option B incorrectly.
Detailed technical explanation
How to think about this question
Under the hood, Azure's prebuilt Object Detection uses a deep neural network (e.g., YOLO or Faster R-CNN) trained on the COCO dataset, which includes categories like 'bed', 'chair', and 'dining table'. The service returns bounding box coordinates and confidence scores for each detected object, enabling precise verification of amenities. In a real-world scenario, the hotel booking site could filter out photos lacking a bed (confidence > 0.8) while accepting those with a desk and chair.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: Object Detection — Object Detection (prebuilt) is the correct choice because it can identify and locate multiple specific objects (bed, desk, chair) within an image by drawing bounding boxes around them. This prebuilt Azure AI Vision feature requires no custom training and directly supports detecting common amenities in hotel room photos.
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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Last reviewed: Jun 11, 2026
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.
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