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AI-102 Practice Question: Which THREE factors should be considered when…
Which THREE factors should be considered when choosing between Azure Computer Vision and Azure Custom Vision? (Choose three.)
⚠ Common exam trap
Microsoft often tests the misconception that edge deployment is exclusive to Custom Vision, but in reality, both services support containerized edge deployment, so the true differentiator is the need for custom retraining and domain-specific 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
✓
The need for custom model retraining over time.
Azure Custom Vision is specifically designed for scenarios where you need to retrain a model over time with new labeled data, such as when the visual characteristics of objects change (e.g., new product packaging). Azure Computer Vision is a pre-trained API that cannot be retrained; it only supports fixed, generic models. Custom Vision allows iterative training with your own images, making it essential when model drift or evolving requirements demand periodic retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The need for custom model retraining over time.
Why this is correct
Custom Vision allows retraining.
- ✗
Whether the solution runs on edge devices.
Why it's wrong here
Both support edge deployment.
- ✓
The amount of labeled training data available.
Why this is correct
Custom Vision requires labeled data.
- ✗
The geographic region of the Azure subscription.
Why it's wrong here
Both services are available globally.
- ✓
Whether the detection objects are generic or domain-specific.
Why this is correct
Custom Vision for domain-specific objects.
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