AI-900 Practice Question: Describe features of computer vision workloads on Azure
A manufacturing company uses overhead cameras on an assembly line to check that each part is present in the correct location on a circuit board. The system must not only confirm the part is there but also draw a box around each part to show its exact position. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse Image Classification (which only labels the whole image) with Object Detection (which provides per-object localization), especially when the question emphasizes both 'confirm the part is there' and 'draw a box around each part'.
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 is the correct capability because it not only identifies whether a specific object (like a circuit board part) is present in an image but also returns bounding box coordinates that indicate the exact location of each detected object. This meets the requirement to both confirm the part's presence and draw a box around it.
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 a computer vision technique that extracts printed or handwritten characters from an image and converts them into machine-readable text. It has no ability to detect or localize non-text objects such as circuit board parts, because it operates on pixel patterns that correspond to letters and digits. On an assembly line, OCR might read a serial number label, but it cannot determine whether a capacitor or connector is present or draw a bounding box around it.
- ✗
Image Classification
Why it's wrong here
Image Classification assigns a single class label to an entire input image, such as "all parts present" or "missing component," using a model that scores the whole image against predefined classes. It does not output spatial information, so it cannot tell an operator or robot where a missing part is located on the board. For a quality-control use case that needs to locate each part and draw boxes around them, classification is insufficient because it provides only a global verdict, not per-object coordinates.
- ✓
Object Detection
Why this is correct
Object Detection is the correct choice because it both identifies instances of objects within an image and returns their bounding-box coordinates, typically as (x, y, width, height) or corner points. On an assembly line, a detector can be trained on labeled images of each circuit board part, and at inference it will output a class label and a box for every detected part. This directly supports locating each part and drawing boxes around them, which neither OCR nor image classification can do.
- ✗
Face Detection
Why it's wrong here
Face Detection is a specialized object-detection variant tuned to identify human faces, usually by leveraging facial landmarks such as eyes, nose, and mouth. Circuit board components do not share these features, and a face detector's learned features are not transferable to inanimate electronic parts. Even if a face detector were run on an assembly image, it would either return no results or produce false positives, making it unsuitable for locating capacitors, resistors, or connectors.
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Azure Machine Learning Studio
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.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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