AI-102 Implement computer vision solutions Practice Question
You deploy a custom vision model for defect detection on a manufacturing line. The model runs on an Azure IoT Edge device. You notice that inference latency is too high for real-time detection. Which action should you take to reduce latency?
⚠ Common exam trap
Candidates often assume cloud-based inference (Option A) is faster due to powerful cloud GPUs, but they overlook the added network latency and the requirement for real-time edge processing in IoT 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
✓
Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration
Converting the model to TensorFlow enables compatibility with the Azure IoT Edge Deep Learning module, which can leverage hardware acceleration (e.g., Intel Movidius or NVIDIA GPUs) to significantly reduce inference latency. This approach keeps inference on the edge device, avoiding network round-trips, and optimizes the model for real-time defect detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Move inference to Azure Functions in the cloud
Why it's wrong here
Cloud inference introduces network latency.
- ✓
Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration
Why this is correct
Hardware acceleration reduces inference time.
- ✗
Retrain the model with more defect images
Why it's wrong here
Does not reduce inference latency.
- ✗
Increase the resolution of input images
Why it's wrong here
Higher resolution increases processing time.
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