- A
Increase the number of CPU cores in the container instance.
Why wrong: CPU inference may still be too slow.
- B
Export the model as a Dockerfile with GPU support and deploy to a GPU-enabled ACI.
GPU acceleration is key for low-latency object detection.
- C
Deploy the model to Azure Functions with a Premium plan.
Why wrong: Azure Functions add cold start and are not optimized for real-time vision.
- D
Use the Cognitive Services Computer Vision container instead.
Why wrong: That container provides general OCR, not custom object detection.
Question 872 of 993
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Custom Vision containers support GPU acceleration via NVIDIA CUDA and cuDNN libraries. When exporting as a Dockerfile with GPU support, the container includes the necessary runtime (e.g., TensorFlow or ONNX Runtime with GPU backend) to offload tensor operations to the GPU. In production, you must also ensure the ACI SKU includes an NVIDIA GPU (e.g., NC-series) and that the container is configured with `--gpus all` to access the device. A common pitfall is forgetting to set the `AZURE_COMPUTER_VISION_GPU` environment variable to `true` in the container, which disables GPU fallback.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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