AI-102 Implement computer vision solutions Practice Question
Your team is building a mobile app that uses Azure Custom Vision to classify plant species. The app must work offline and sync labeled images when connectivity is restored. Which SDK feature should you use?
⚠ Common exam trap
A common mix-up: candidates confuse offline inference with edge computing (IoT Edge) or API caching, not realizing that Custom Vision's export feature is the only option that provides a local model for on-device classification without requiring a network connection.
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 a TensorFlow or CoreML model for on-device inference
Azure Custom Vision allows exporting trained models to formats like TensorFlow, CoreML, ONNX, or Docker for on-device inference. This enables the mobile app to run classification locally without network connectivity, and the Custom Vision SDK includes a method to upload labeled images for offline training sync when connectivity is restored.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure IoT Edge runtime on the phone
Why it's wrong here
IoT Edge is not for mobile phones.
- ✗
Azure API Management with caching
Why it's wrong here
Caching helps but requires initial connection.
- ✓
Export the model as a TensorFlow or CoreML model for on-device inference
Why this is correct
Exported model runs offline.
- ✗
Continuous deployment integration
Why it's wrong here
CD is for cloud deployments.
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This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.