- A
Exporting model training logs and metrics to Excel for analysis
Why wrong: Metrics export is reporting — model export in Custom Vision means packaging the model for offline/edge inference.
- B
Exporting trained models as ONNX, TensorFlow, CoreML, or Docker for offline/edge deployment
Custom Vision export enables edge AI — shipping the model to devices where cloud calls aren't possible or desirable.
- C
Exporting the training data to another Azure service for fine-tuning
Why wrong: Training data portability is data management — model export exports the trained model weights for inference deployment.
- D
Exporting a Custom Vision project as a YAML configuration file for source control
Why wrong: Project configuration export may be supported, but model export specifically means exporting trained model weights in inference formats.
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 'model export' in Azure Custom Vision and what formats are supported?
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
Exporting trained models as ONNX, TensorFlow, CoreML, or Docker for offline/edge deployment
Model export in Azure Custom Vision allows you to export a trained image classification or object detection model in formats like ONNX, TensorFlow, CoreML, or Docker container images. This enables the model to run offline on edge devices or local servers without requiring a continuous connection to the Azure cloud, which is critical for low-latency or disconnected scenarios.
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.
- ✗
Exporting model training logs and metrics to Excel for analysis
Why it's wrong here
Metrics export is reporting — model export in Custom Vision means packaging the model for offline/edge inference.
- ✓
Exporting trained models as ONNX, TensorFlow, CoreML, or Docker for offline/edge deployment
Why this is correct
Custom Vision export enables edge AI — shipping the model to devices where cloud calls aren't possible or desirable.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Exporting the training data to another Azure service for fine-tuning
Why it's wrong here
Training data portability is data management — model export exports the trained model weights for inference deployment.
- ✗
Exporting a Custom Vision project as a YAML configuration file for source control
Why it's wrong here
Project configuration export may be supported, but model export specifically means exporting trained model weights in inference formats.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'model export' with exporting training data or logs, because Azure Custom Vision does offer data export options elsewhere, but the specific term 'model export' refers exclusively to the trained model artifact for offline deployment.
Detailed technical explanation
How to think about this question
Under the hood, model export compiles the trained neural network weights and architecture into platform-specific formats: ONNX provides cross-framework interoperability, TensorFlow SavedModel is optimized for mobile and embedded devices, CoreML integrates with Apple's ecosystem, and Docker containers package the model with a runtime for scalable edge deployment. A subtle behavior is that exported models may have slight accuracy differences due to quantization or operator support variations across formats, so validation on the target device is recommended.
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: Exporting trained models as ONNX, TensorFlow, CoreML, or Docker for offline/edge deployment — Model export in Azure Custom Vision allows you to export a trained image classification or object detection model in formats like ONNX, TensorFlow, CoreML, or Docker container images. This enables the model to run offline on edge devices or local servers without requiring a continuous connection to the Azure cloud, which is critical for low-latency or disconnected scenarios.
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
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