The answer is whether the detection objects are generic or domain-specific. This is the correct selection factor because Azure Computer Vision provides pre-trained, fixed models for common scenarios like landmark detection or OCR, which cannot be retrained, while Azure Custom Vision is built for iterative training on your own labeled images, making it essential when objects are unique or evolve over time, such as new product packaging or specialized industrial defects. On the AI-102 exam, this distinction tests your understanding of when to use a no-code, customizable model versus a static API, and a common trap is assuming Computer Vision can be fine-tuned—it cannot. A useful memory tip is to think of "Custom" as "Customizable" for domain-specific needs, and "Computer" as "Common" for generic, out-of-the-box tasks.
AI-102 Practice Question: Implement image and video processing solutions
This AI-102 practice question tests your understanding of implement image and video processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 factors should be considered when choosing between Azure Computer Vision and Azure Custom Vision? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The need for custom model retraining over time.
Option A is correct because Azure Custom Vision is specifically designed for scenarios where you need to retrain a model over time with new labeled data, such as when the visual characteristics of objects change (e.g., new product packaging). Azure Computer Vision is a pre-trained API that cannot be retrained; it only supports fixed, generic models. Custom Vision allows iterative training with your own images, making it essential when model drift or evolving requirements demand periodic retraining.
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.
✓
The need for custom model retraining over time.
Why this is correct
Custom Vision allows retraining.
Related concept
Read the scenario before looking for a memorised answer.
✗
Whether the solution runs on edge devices.
Why it's wrong here
Both support edge deployment.
✓
The amount of labeled training data available.
Why this is correct
Custom Vision requires labeled data.
Related concept
Read the scenario before looking for a memorised answer.
✗
The geographic region of the Azure subscription.
Why it's wrong here
Both services are available globally.
✓
Whether the detection objects are generic or domain-specific.
Why this is correct
Custom Vision for domain-specific objects.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that edge deployment is exclusive to Custom Vision, but in reality, both services support containerized edge deployment, so the true differentiator is the need for custom retraining and domain-specific detection.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision uses a fixed set of deep neural networks trained on millions of generic images, exposing REST APIs for tasks like OCR and object detection without any training pipeline. Custom Vision, built on transfer learning, allows you to upload labeled images (minimum 15 per tag) and triggers a training job that fine-tunes a ResNet-based model, outputting a custom iteration endpoint. In a real-world scenario, a factory using Custom Vision to detect defective parts would need periodic retraining as new defect types emerge, while Computer Vision would fail because it cannot learn new classes.
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 image and video processing solutions — This question tests Implement image and video processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The need for custom model retraining over time. — Option A is correct because Azure Custom Vision is specifically designed for scenarios where you need to retrain a model over time with new labeled data, such as when the visual characteristics of objects change (e.g., new product packaging). Azure Computer Vision is a pre-trained API that cannot be retrained; it only supports fixed, generic models. Custom Vision allows iterative training with your own images, making it essential when model drift or evolving requirements demand periodic retraining.
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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