- A
Azure AI Vision is faster; Custom Vision is more accurate
Why wrong: Performance depends on the use case — the key difference is pre-built general purpose (Vision) vs. custom-trained models (Custom Vision).
- B
Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images
AI Vision = ready-to-use general models; Custom Vision = train your own specialized models with your own labeled data.
- C
Azure AI Vision analyzes only photos; Custom Vision analyzes documents
Why wrong: Both work with images — the distinction is pre-built vs. custom-trained models.
- D
They are different names for the same service
Why wrong: They are distinct services with different capabilities — Vision is pre-built general; Custom Vision trains custom models.
Quick Answer
The answer is that Azure AI Vision offers pre-built models while Custom Vision trains custom models on your labeled images. This is correct because Azure AI Vision provides ready-to-use, pre-trained models for common tasks like image analysis, OCR, and facial recognition without requiring any training data, whereas Azure AI Custom Vision is a separate service designed to let you upload your own labeled images to train a model for specific classification or object detection problems. On the Microsoft Azure AI Fundamentals AI-900 exam, this distinction tests your understanding of when to use a pre-built solution versus a customizable training platform—a common trap is confusing Custom Vision as a feature within Azure AI Vision rather than a distinct tool. Remember the memory tip: “Pre-built for the common, Custom for the uncommon.”
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 difference between Azure AI Vision and Azure AI Custom Vision?
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
Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images
Azure AI Vision provides pre-built, ready-to-use models for common computer vision tasks like image analysis, OCR, and facial recognition, requiring no training data. Azure AI Custom Vision, on the other hand, allows you to train custom models using your own labeled images to solve specific classification or object detection problems. This distinction makes option B correct because it accurately captures the core difference between a pre-built service and a customizable training platform.
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.
- ✗
Azure AI Vision is faster; Custom Vision is more accurate
Why it's wrong here
Performance depends on the use case — the key difference is pre-built general purpose (Vision) vs. custom-trained models (Custom Vision).
- ✓
Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images
Why this is correct
AI Vision = ready-to-use general models; Custom Vision = train your own specialized models with your own labeled data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure AI Vision analyzes only photos; Custom Vision analyzes documents
Why it's wrong here
Both work with images — the distinction is pre-built vs. custom-trained models.
- ✗
They are different names for the same service
Why it's wrong here
They are distinct services with different capabilities — Vision is pre-built general; Custom Vision trains custom models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'pre-built' with 'faster' or 'more accurate,' or assume both services are interchangeable, when in fact the key differentiator is whether you need to provide your own labeled training data (Custom Vision) or can rely on Microsoft's pre-trained models (Azure AI Vision).
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision uses deep neural networks pre-trained on massive datasets (e.g., ImageNet) to extract features like tags, captions, and landmarks without any user-provided training data. Custom Vision leverages transfer learning, allowing you to fine-tune a pre-trained model (such as ResNet or MobileNet) on your own labeled images, which dramatically reduces the amount of data and training time needed compared to training from scratch. In a real-world scenario, a retail company might use Azure AI Vision for out-of-the-box product recognition, then switch to Custom Vision to identify specific store-brand items that the pre-built model cannot recognize.
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-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: Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images — Azure AI Vision provides pre-built, ready-to-use models for common computer vision tasks like image analysis, OCR, and facial recognition, requiring no training data. Azure AI Custom Vision, on the other hand, allows you to train custom models using your own labeled images to solve specific classification or object detection problems. This distinction makes option B correct because it accurately captures the core difference between a pre-built service and a customizable training platform.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is the difference between Azure AI Vision and Azure AI Custom Vision in terms of when to use each?
medium- A.Use Azure AI Vision for large images; use Custom Vision for small images
- ✓ B.Use Azure AI Vision for general image analysis; use Custom Vision when you need specialized domain-specific recognition
- C.Use Azure AI Vision only in production; Custom Vision only in development
- D.Use Azure AI Vision for images from cameras; Custom Vision for images from documents
Why B: Azure AI Vision is a pre-trained service for general image analysis tasks like object detection, OCR, and description generation, requiring no custom training. Azure AI Custom Vision allows you to train a model on your own labeled images for specialized, domain-specific recognition tasks, such as identifying unique product defects or rare animal species. Option B correctly captures this distinction: use Azure AI Vision for broad, out-of-the-box capabilities and Custom Vision when you need tailored recognition for your specific use case.
Variation 2. What is 'Azure AI Custom Vision' and how does it differ from Azure AI Vision?
medium- A.Azure AI Vision is for video; Custom Vision is for still images only
- ✓ B.Azure AI Vision offers pre-built general models; Custom Vision lets you train models for your specific categories
- C.Custom Vision is more expensive because it uses more advanced AI algorithms
- D.Azure AI Vision requires GPU compute; Custom Vision runs on CPU only
Why B: Azure AI Vision provides pre-trained models for common computer vision tasks like object detection, OCR, and image analysis without requiring custom training data. Azure AI Custom Vision, on the other hand, allows you to upload your own labeled images and train a model to recognize specific categories or objects that are unique to your business scenario. This distinction makes B correct because it highlights the key difference: pre-built general models versus custom-trained models.
Last reviewed: Jun 11, 2026
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