The answer is to increase the training dataset size with more varied images. This directly addresses overfitting in custom vision models, where a small dataset of only 200 images per class causes the model to memorize noise and specific details rather than learning generalizable patterns, resulting in high training accuracy but poor test performance. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of model optimization and data augmentation strategies for Custom Vision, a common trap being the temptation to increase training epochs—which worsens overfitting—or to adjust learning rates, which risks divergence. A key memory tip is to think of overfitting as “memorizing the menu, not the cuisine”; adding diverse, real-world images forces the model to learn robust features instead of rote recall.
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.
You are deploying a computer vision model using Azure AI Custom Vision with a small dataset of 200 images per class. The model shows high accuracy on training data but low accuracy on test data. Which action should you take to reduce overfitting?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the training dataset size with more varied images
The correct answer is to increase the training dataset size by adding more varied images. Overfitting occurs when the model learns noise from a small dataset. Option A is wrong because increasing the number of training epochs can actually increase overfitting. Option B is wrong because increasing the learning rate may cause divergence. Option D is wrong because reducing image size can lose important features.
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.
✗
Increase the learning rate
Why it's wrong here
A higher learning rate may cause the model to converge poorly, not reduce overfitting.
✗
Reduce the image size to lower resolution
Why it's wrong here
Reducing resolution may discard important details and does not address overfitting.
✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting on a small dataset.
✓
Increase the training dataset size with more varied images
Why this is correct
More data helps the model generalize better and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Increase the training dataset size with more varied images — The correct answer is to increase the training dataset size by adding more varied images. Overfitting occurs when the model learns noise from a small dataset. Option A is wrong because increasing the number of training epochs can actually increase overfitting. Option B is wrong because increasing the learning rate may cause divergence. Option D is wrong because reducing image size can lose important features.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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