AI-900 Practice Question: Describe features of computer vision workloads on Azure
A manufacturing company uses Azure Computer Vision to analyze assembly line images. They need to identify specific product defects (e.g., scratches, dents) and also read serial numbers printed on the products in various fonts. Which combination of Azure Computer Vision features should they use?
⚠ Common exam trap
Watch out — candidates often assume the built-in Image Analysis object detection can be customized for defects, but it is a pre-trained general model, whereas Custom Vision is required for custom training.
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
✓
Custom Vision (object detection) and OCR
The scenario requires two distinct capabilities: identifying specific defect types (scratches, dents) and reading variable-font serial numbers. Custom Vision's object detection model can be trained on labeled defect images to recognize those specific patterns, while Azure's OCR (part of Computer Vision's Read API) extracts printed text regardless of font. Combining these two features directly addresses both requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image Analysis (object detection) and OCR
Why it's wrong here
The prebuilt Image Analysis object detection model recognizes common objects (e.g., person, car, bottle) but is not trained on manufacturing defects such as scratches, cracks, or dents. Even when it successfully frames the product in an image, it cannot localize or classify the anomaly itself, so the defect-detection requirement remains unmet. The OCR component correctly reads the serial number, but that only solves the identification half of the task.
- ✓
Custom Vision (object detection) and OCR
Why this is correct
Custom Vision's object detection capability lets you train a model on your own labeled dataset of defective and non-defective parts, producing bounding boxes around each defect with confidence scores. This transfer-learning approach adapts to your specific product line, unlike generic prebuilt services. OCR is then used in parallel to extract the serial number, giving you a pipeline that both locates flaws and reads the identifier—fully satisfying the stated requirements.
- ✗
Face API and OCR
Why it's wrong here
Face API is specialized for human-face analysis—detection, recognition, age, emotion—and has no understanding of industrial artifacts, so it cannot flag scratches or dents on a manufactured part. Pairing it with OCR only adds text extraction, which is valuable for serial numbers but does nothing to inspect the product's surface integrity. Consequently, the defect-detection requirement is completely unaddressed by this combination.
- ✗
Image Analysis (tags) and OCR
Why it's wrong here
The Image Analysis tags feature classifies the entire image with descriptive labels (e.g., 'metal', 'broken'), but it returns no bounding boxes or spatial coordinates, so you cannot pinpoint where a defect occurs on the part. This makes it impossible to separate a true defect from a benign visual feature, and localization is essential for downstream rework or rejection. OCR handles the serial number, but without defect localization, the quality-inspection goal is not achieved.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
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
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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