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AI-102 Practice Question: A company uses Azure Custom Vision to classify…
A company uses Azure Custom Vision to classify images of defective parts. After deploying the model, the accuracy is low. The team only has 10 images per class. What is the most effective way to improve accuracy?
⚠ Common exam trap
Watch out — candidates often assume increasing epochs or changing the algorithm will fix low accuracy, but the real bottleneck is insufficient and non-diverse training data, which is the most common cause of poor Custom Vision model performance.
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
✓
Add at least 50 more images per class with variations.
Azure Custom Vision relies on deep learning models that require a sufficient number of diverse training images to generalize well. With only 10 images per class, the model is severely underfit and prone to overfitting; adding at least 50 more images per class with variations in lighting, angle, and background provides the necessary data diversity to improve accuracy significantly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different classification algorithm.
Why it's wrong here
Custom Vision uses advanced algorithms; data is the issue.
- ✓
Add at least 50 more images per class with variations.
Why this is correct
More data improves model accuracy.
- ✗
Reduce the image resolution to speed up training.
Why it's wrong here
Does not improve accuracy.
- ✗
Increase the number of training iterations (epochs).
Why it's wrong here
May overfit with small dataset.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.