AI-102 Implement computer vision solutions Practice Question
You are designing a solution to detect brand logos in social media images. The logos vary in size and orientation. You need to achieve high accuracy with minimal false positives. Which approach should you recommend?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Computer Vision's pre-built domain-specific models (which cover only landmarks, celebrities, and general objects) with the ability to detect custom logos, leading them to choose option C instead of recognizing that Custom Vision is required for custom object detection.
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
✓
Train an Azure Custom Vision object detection model with labeled logo images.
Azure Custom Vision allows you to train a custom object detection model with your own labeled dataset of brand logos, enabling high accuracy for specific logo shapes, sizes, and orientations. This approach directly addresses the need for minimal false positives by learning the exact visual features of the logos, unlike generic pre-built models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure Computer Vision Describe API to generate captions and filter by logo mentions.
Why it's wrong here
The Describe API returns natural-language captions, which cannot reliably name specific brands or localise them, producing missed detections and false positives. It is tempting because captioning is quick to implement, and would be correct for generating alt text or general image summaries.
- ✓
Train an Azure Custom Vision object detection model with labeled logo images.
Why this is correct
Object detection returns bounding boxes, so it handles logos that vary in size and orientation, unlike classification which only labels the whole image. Training on your labelled logo images tunes the model to your brands, satisfying the high-accuracy, low-false-positive constraint.
- ✗
Use Azure Computer Vision Analyze API with domain-specific models.
Why it's wrong here
Domain-specific models cover fixed categories such as celebrities and landmarks, not arbitrary brand logos varying in size and orientation. It is tempting because it is a ready-made vision feature, but it would be correct only for those predefined domains, not custom logo detection requiring a trained object-detection model.
- ✗
Use Azure Form Recognizer to extract logo positions from images.
Why it's wrong here
Form Recognizer targets structured documents — invoices, receipts, forms — extracting text and key-value pairs, not arbitrary logos at varying scales and rotations. It is tempting because it excels at positional extraction within documents, and would be correct for parsing scanned forms or contracts, not free-form social media imagery.
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