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.
Which THREE actions can be performed using the Azure Custom Vision service?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Export a trained model to ONNX format for offline inference.
Option B is correct because Azure Custom Vision allows you to export trained models to ONNX format for offline inference. This enables running the model on edge devices or in environments without continuous internet connectivity, leveraging the ONNX runtime for efficient deployment.
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.
✗
Extract text from scanned receipts.
Why it's wrong here
This is a Form Recognizer capability.
✓
Export a trained model to ONNX format for offline inference.
Why this is correct
Custom Vision allows export to ONNX, TensorFlow, etc.
Related concept
Read the scenario before looking for a memorised answer.
✓
Train a model to classify images of different product types.
Why this is correct
Custom Vision supports image classification.
Related concept
Read the scenario before looking for a memorised answer.
✓
Detect and locate multiple objects in an image with bounding boxes.
Why this is correct
Custom Vision supports object detection.
Related concept
Read the scenario before looking for a memorised answer.
✗
Identify specific individuals in a crowd using facial recognition.
Why it's wrong here
Facial recognition is part of Face API, not Custom Vision.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure Custom Vision's capabilities with other Azure AI services, mistakenly thinking it handles OCR (like Form Recognizer) or facial recognition (like Face API), when Custom Vision is strictly for custom image classification and object detection.
Detailed technical explanation
How to think about this question
Azure Custom Vision exports models to ONNX, TensorFlow, CoreML, and Docker formats, with ONNX being a cross-platform format that optimizes inference on CPUs and GPUs. Under the hood, the exported model includes the trained neural network weights and architecture, allowing integration with ONNX Runtime for low-latency predictions in offline scenarios, such as on IoT devices or mobile apps. A real-world example is deploying a product classification model on a retail store's local server to classify items without cloud dependency.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 a trained model to ONNX format for offline inference. — Option B is correct because Azure Custom Vision allows you to export trained models to ONNX format for offline inference. This enables running the model on edge devices or in environments without continuous internet connectivity, leveraging the ONNX runtime for efficient deployment.
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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Question Discussion
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