The answer is Object Detection and Semantic Segmentation. Object Detection is correct because it identifies and locates specific defect types like scratches or cracks by drawing bounding boxes around each instance, directly fulfilling the requirement to both identify and locate defects. Semantic Segmentation goes a step further by classifying every pixel in the image, which is essential when defects have irregular shapes or boundaries that a simple box cannot capture—for example, precisely outlining a crack’s path. On the AI-900 exam, this question tests your understanding of how Azure Computer Vision capabilities map to real-world manufacturing scenarios; a common trap is choosing only Object Detection and forgetting that Semantic Segmentation provides the pixel-level precision needed for non-rectangular defects. A useful memory tip: think of Object Detection as “what and where in a box,” while Semantic Segmentation is “what and where for every single pixel.”
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 manufacturing company wants to use Azure Computer Vision to automatically inspect products on an assembly line for defects. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capabilities could be used together to achieve this? (Select two options.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Object Detection
Option A is correct because Object Detection in Azure Computer Vision can identify and locate multiple specific defect types (e.g., scratch, dent, crack) within product images by drawing bounding boxes around each defect. This capability directly meets the requirement to both identify and locate defects on the assembly line.
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.
✓
Object Detection
Why this is correct
Object Detection identifies and locates multiple objects (defects) in an image with bounding boxes.
Related concept
Read the scenario before looking for a memorised answer.
✓
Semantic Segmentation
Why this is correct
Semantic Segmentation classifies each pixel, allowing detailed mapping of defect regions like scratches or dents.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, not relevant for defect detection.
✗
Image Classification
Why it's wrong here
Image Classification assigns a single label to an entire image, which cannot locate multiple defect types.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Image Classification with Object Detection, not realizing that classification cannot locate multiple defects or distinguish between defect types in a single image.
Detailed technical explanation
How to think about this question
Object Detection uses deep learning models (e.g., Faster R-CNN or YOLO) trained on labeled bounding boxes to output both class labels and coordinates for each detected object. Semantic Segmentation, on the other hand, classifies every pixel in the image into a category (e.g., 'scratch', 'background'), providing pixel-level defect masks that are useful for measuring defect area or shape. In a real-world assembly line, combining these allows the system to not only find defects but also quantify their size and severity.
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: Object Detection — Option A is correct because Object Detection in Azure Computer Vision can identify and locate multiple specific defect types (e.g., scratch, dent, crack) within product images by drawing bounding boxes around each defect. This capability directly meets the requirement to both identify and locate defects on the assembly line.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company wants to use Azure Computer Vision to automatically monitor shelf inventory. They need to detect whether items are present on a shelf and count the number of items, without needing to identify the specific product type. Which prebuilt Computer Vision capability should they use?
easy
A.Optical Character Recognition (OCR)
B.Image classification
✓ C.Object detection
D.Semantic segmentation
Why C: Object detection (Option C) is the correct prebuilt Computer Vision capability because it can both locate items within an image using bounding boxes and count them, without requiring identification of the specific product type. This aligns directly with the requirement to detect presence and count items on a shelf, as object detection outputs the coordinates and count of detected objects, not their classification into fine-grained categories.
Variation 2. A retail company wants to use security cameras to automatically detect when products are removed from shelves. They need to identify the specific product type (e.g., a cereal box, a soda can) and count how many units are taken. Which Azure Computer Vision capability should they use?
medium
A.Optical Character Recognition (OCR)
✓ B.Object detection
C.Image tagging
D.Face detection
Why B: Object detection is the correct capability because it can both locate objects within an image (via bounding boxes) and classify them into specific categories (e.g., cereal box, soda can). This allows the system to identify the product type and count the number of units removed from shelves, which aligns directly with the requirement.
Variation 3. A retail chain wants to automatically detect which specific products are missing from store shelves by analyzing images from in-store cameras. Each product has a distinct shape and label. Which Azure Computer Vision capability is most appropriate for this task?
medium
A.A) Image Classification
✓ B.B) Object Detection
C.C) Optical Character Recognition (OCR)
D.D) Facial Recognition
Why B: Object Detection (Option B) is the correct choice because it can identify and locate multiple products within an image by drawing bounding boxes around each detected object. This allows the system to determine which specific products are missing by comparing detected items against an expected inventory list. Image Classification would only label the entire image, not individual products, while OCR focuses on text extraction and Facial Recognition identifies people.
Variation 4. A transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?
medium
A.Object detection
✓ B.Image classification
C.Optical Character Recognition (OCR)
D.Semantic segmentation
Why B: Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.
Last reviewed: Jun 11, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.