- A
Availability of labeled training data specific to the domain
Custom Vision requires labeled data; pre-built models do not.
- B
Image format support (JPEG, PNG)
Why wrong: Both services support common formats.
- C
Whether the required labels are covered by the pre-built model
If labels are covered, pre-built models are easier; otherwise, custom is needed.
- D
Need for real-time inference latency
Why wrong: Both services offer low latency; it's not a distinguishing factor.
- E
Need to iterate and retrain the model over time
Custom Vision allows iterative retraining with new data; pre-built models are static.
Question 33 of 993
When to Use Custom Vision vs Pre-built Vision Models: 3 Factors
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 factors should you consider when choosing between Azure AI Custom Vision and Azure AI Vision pre-built models for an image classification task? (Choose three.)
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
✓
Availability of labeled training data specific to the domain
Option A is correct because Azure AI Custom Vision is specifically designed for scenarios where you have domain-specific labeled training data that is not covered by pre-built models. Custom Vision allows you to upload your own labeled images and train a custom model tailored to your unique classification needs, which is essential when off-the-shelf models fail to recognize your target classes.
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.
- ✓
Availability of labeled training data specific to the domain
Why this is correct
Custom Vision requires labeled data; pre-built models do not.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Image format support (JPEG, PNG)
Why it's wrong here
Both services support common formats.
- ✓
Whether the required labels are covered by the pre-built model
Why this is correct
If labels are covered, pre-built models are easier; otherwise, custom is needed.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Need for real-time inference latency
Why it's wrong here
Both services offer low latency; it's not a distinguishing factor.
- ✓
Need to iterate and retrain the model over time
Why this is correct
Custom Vision allows iterative retraining with new data; pre-built models are static.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse image format support or latency as key differentiators, when in fact both services handle these similarly, and the core distinction lies in the availability of custom labeled data and the need for iterative retraining.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Custom Vision uses transfer learning from a pre-trained deep neural network (e.g., ResNet or MobileNet) and fine-tunes it on your custom dataset, which requires labeled training data. In contrast, pre-built models are fixed and cannot be retrained; they rely on Microsoft's large-scale training on generic datasets. A real-world scenario is a medical imaging startup needing to classify rare diseases—Custom Vision is necessary because pre-built models lack those specific labels, and the startup can iterate on the model as new labeled data becomes available.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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-102 question test?
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: Availability of labeled training data specific to the domain — Option A is correct because Azure AI Custom Vision is specifically designed for scenarios where you have domain-specific labeled training data that is not covered by pre-built models. Custom Vision allows you to upload your own labeled images and train a custom model tailored to your unique classification needs, which is essential when off-the-shelf models fail to recognize your target classes.
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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Last reviewed: Jul 4, 2026
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