AI-102 Implement computer vision solutions Practice Question
You are building a mobile app that allows users to take a photo of a product and get detailed information. The app uses Azure AI Custom Vision to classify products. You need to ensure low latency for inference. What should you do?
⚠ Common exam trap
Test-takers frequently assume cloud-based solutions (like Azure Front Door or Vision API) are always faster, but Microsoft explicitly tests the understanding that on-device inference eliminates network latency and is the optimal choice for low-latency mobile scenarios.
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
✓
Export the Custom Vision model as a TensorFlow model and run on-device
Exporting the Custom Vision model as a TensorFlow model and running it on-device eliminates network latency entirely. Inference happens locally on the mobile device, which provides the lowest possible latency for real-time classification, especially when network connectivity is poor or inconsistent.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of training iterations
Why it's wrong here
More iterations may improve accuracy but not inference latency.
- ✗
Use the Azure AI Vision API directly
Why it's wrong here
API calls introduce network latency.
- ✗
Use Azure Front Door to cache results
Why it's wrong here
Caching is for repeated queries, but each product may be unique.
- ✓
Export the Custom Vision model as a TensorFlow model and run on-device
Why this is correct
On-device inference is fastest; TensorFlow Lite can run on mobile.
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Written by Johnson Ajibi, MSc IT Security
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