AI-102 Implement computer vision solutions Practice Question
You are deploying a Custom Vision object detection model to an Azure Container Instance for real-time inference. The model must respond within 500 ms. The default container runs on CPU. What should you do to meet the latency requirement?
⚠ Common exam trap
Test-takers frequently assume increasing CPU cores (Option A) is a valid performance fix, but Azure explicitly documents that Custom Vision object detection models require GPU acceleration for real-time latency under 500 ms, and the default CPU container is only suitable for batch or offline processing.
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 Dockerfile with GPU support and deploy to a GPU-enabled ACI.
The default Custom Vision container runs on CPU, which is insufficient for real-time object detection inference within 500 ms. Exporting the model as a Dockerfile with GPU support and deploying to a GPU-enabled Azure Container Instance (ACI) leverages NVIDIA CUDA-accelerated inference, dramatically reducing latency to meet the sub-500 ms requirement.
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 CPU cores in the container instance.
Why it's wrong here
CPU inference may still be too slow.
- ✓
Export the model as a Dockerfile with GPU support and deploy to a GPU-enabled ACI.
Why this is correct
GPU acceleration is key for low-latency object detection.
- ✗
Deploy the model to Azure Functions with a Premium plan.
Why it's wrong here
Azure Functions add cold start and are not optimized for real-time vision.
- ✗
Use the Cognitive Services Computer Vision container instead.
Why it's wrong here
That container provides general OCR, not custom object detection.
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