- A
Training models on a compact (small) dataset with fewer than 50 images
Why wrong: Compact domains are about model size for edge deployment — not about dataset size.
- B
Producing exportable models optimized for deployment on edge devices with limited compute
Compact domains create smaller, exportable model files (ONNX, TensorFlow Lite, CoreML) that run offline on edge devices.
- C
Creating more compact API responses with less metadata
Why wrong: API response format is API design — compact domains produce exportable, small model files for edge devices.
- D
Training models that use less storage in Azure blob containers
Why wrong: Cloud storage optimization is separate — compact domains create models suitable for offline deployment on resource-constrained devices.
Quick Answer
The correct answer is that the Azure AI Custom Vision service's 'compact' domain is used for producing exportable models optimized for deployment on edge devices with limited compute. This domain achieves this by trading some accuracy for a significantly smaller model footprint, enabling real-time inference on resource-constrained hardware like cameras, drones, and IoT gateways. On the AI-900 exam, this concept tests your understanding of how Azure balances model performance against deployment constraints; a common trap is confusing the compact domain with the general domain, which prioritizes cloud-based accuracy over portability. Remember the mnemonic "CED" — Compact for Edge Devices — to recall that this domain is your go-to when you need to export a model to formats like TensorFlow or ONNX for offline, low-power scenarios.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.
What is the Azure AI Custom Vision service's 'compact' domain used for?
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
Producing exportable models optimized for deployment on edge devices with limited compute
The Azure AI Custom Vision service's 'compact' domain is specifically designed to produce models that can be exported to formats like TensorFlow, ONNX, or CoreML for deployment on edge devices with limited compute, memory, and power. This domain trades some accuracy for a smaller model footprint, enabling real-time inference on devices such as cameras, drones, or IoT gateways.
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.
- ✗
Training models on a compact (small) dataset with fewer than 50 images
Why it's wrong here
Compact domains are about model size for edge deployment — not about dataset size.
- ✓
Producing exportable models optimized for deployment on edge devices with limited compute
Why this is correct
Compact domains create smaller, exportable model files (ONNX, TensorFlow Lite, CoreML) that run offline on edge devices.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Creating more compact API responses with less metadata
Why it's wrong here
API response format is API design — compact domains produce exportable, small model files for edge devices.
- ✗
Training models that use less storage in Azure blob containers
Why it's wrong here
Cloud storage optimization is separate — compact domains create models suitable for offline deployment on resource-constrained devices.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'compact' with 'small dataset' or 'reduced API output', when in fact it specifically refers to the model's exportability and optimization for offline edge deployment.
Detailed technical explanation
How to think about this question
Under the hood, compact domains use a smaller neural network backbone (e.g., MobileNet or SqueezeNet) with fewer parameters, which reduces the model file size to as low as a few megabytes. This allows the exported model to run on edge devices with limited RAM (e.g., 256 MB) and no GPU, using frameworks like TensorFlow Lite or ONNX Runtime. A real-world scenario is deploying a defect detection model on a Raspberry Pi in a factory, where the compact domain enables sub-100ms inference per image without cloud connectivity.
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Producing exportable models optimized for deployment on edge devices with limited compute — The Azure AI Custom Vision service's 'compact' domain is specifically designed to produce models that can be exported to formats like TensorFlow, ONNX, or CoreML for deployment on edge devices with limited compute, memory, and power. This domain trades some accuracy for a smaller model footprint, enabling real-time inference on devices such as cameras, drones, or IoT gateways.
What should I do if I get this AI-900 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: Jun 11, 2026
This AI-900 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-900 exam.
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