AI-102 Implement computer vision solutions Practice Question
A company uses Azure AI Custom Vision to classify images of products. The model is deployed to a mobile app. The app sends images to the Custom Vision prediction endpoint. Users report that the app is slow when the network is poor. You need to enable offline inference on the mobile device. What should you do?
⚠ Common exam trap
Many candidates confuse reducing latency (e.g., smaller images or timeouts) with enabling offline inference, which requires exporting the model to a format that runs locally on the device.
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 model as TensorFlow Lite and integrate it into the mobile app
Exporting the Custom Vision model as TensorFlow Lite allows it to run directly on the mobile device, enabling offline inference without requiring network connectivity. This eliminates latency from poor network conditions by processing images locally, which is the only way to achieve true offline inference in a mobile app.
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 the Custom Vision compact model with smaller image size
Why it's wrong here
Reduces latency but still requires network.
- ✓
Export the model as TensorFlow Lite and integrate it into the mobile app
Why this is correct
Enables on-device inference without network.
- ✗
Increase the prediction API timeout
Why it's wrong here
Does not enable offline inference.
- ✗
Deploy the model to an Azure IoT Edge device
Why it's wrong here
Not suitable for mobile app offline use.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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