AI-900 Practice Question: Describe features of computer vision workloads on Azure
A quality control team uses computer vision to inspect manufactured parts. They need to detect whether a part has any defects and also identify the type of defect (e.g., scratch, crack, dent) from an image. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse image classification with object detection, assuming that classifying the entire image as 'defective' is sufficient, but the question explicitly requires identifying the type of each defect, which necessitates localization and multi-class output.
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
✓
B: Object detection
Object detection is the correct capability because it not only identifies the presence of defects in an image but also localizes each defect with a bounding box and classifies it into specific types (e.g., scratch, crack, dent). This meets both requirements: detecting whether a part has defects and identifying the type of each defect.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A: Image classification
Why it's wrong here
Image classification assigns a single class label to the entire image, so it cannot localize defects or distinguish multiple defect types in one scene. For a product containing both a scratch and a dent, the model would return only one label or a probability distribution, losing the spatial information needed to guide repair or rejection. It also cannot count instances, making it inadequate for quality inspection workflows that require per-defect type and location.
- ✓
B: Object detection
Why this is correct
Object detection is correct because it jointly performs localization and classification by drawing bounding boxes around each defect and labeling it (e.g., 'scratch' or 'dent'). This enables quality teams to count multiple defect instances, identify their types, and record their exact coordinates for automated downstream actions. It is the standard computer vision task used in manufacturing defect inspection, including with Azure Custom Vision's object detection model.
- ✗
C: Semantic segmentation
Why it's wrong here
Semantic segmentation classifies every pixel into a class, producing a precise pixel-level mask of defect regions rather than discrete bounding boxes. However, it does not separate individual instances of the same class — two adjacent scratches merge into a single mask, so counting defects is not straightforward. Training also requires expensive pixel-accurate annotations, which is overkill when simply detecting and counting defect types with a bounding box is sufficient for quality control.
- ✗
D: Optical character recognition (OCR)
Why it's wrong here
Optical character recognition (OCR) is designed to extract text characters from images, such as serial numbers or part labels. It has no visual feature detectors for surface anomalies like scratches, color deviations, or structural deformations, so it cannot perform defect classification or localization. Applying OCR to visual quality inspection is a fundamental mismatch of capabilities, as OCR models are trained to recognize glyph shapes, not manufacturing flaws.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
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.
About these practice questions
One of 985 original AI-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.