Quick Answer
The correct answer is to add at least 50 more images per class with variations. This is because Azure Custom Vision’s deep learning models require a critical mass of diverse training data to generalize effectively; with only 10 images per class, the model is severely underfit and prone to overfitting, memorizing those few examples rather than learning the true features of defective parts. On the AI-102 exam, this scenario tests your understanding of data quantity and diversity as the primary lever for improving Custom Vision accuracy, often appearing as a trap where candidates mistakenly choose hyperparameter tuning or transfer learning first. The key insight is that no algorithm can compensate for insufficient or homogeneous data—adding images with varied lighting, angles, and backgrounds directly addresses the root cause. Memory tip: think “50 to thrive”—fifty diverse images per class is the minimum threshold to move from memorization to genuine pattern recognition.
This AI-102 practice question tests your understanding of implement image and video processing 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.
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?
Use a different classification algorithm.
Why wrong: Custom Vision uses advanced algorithms; data is the issue.
Add at least 50 more images per class with variations.
More data improves model accuracy.
Reduce the image resolution to speed up training.
Why wrong: Does not improve accuracy.
Increase the number of training iterations (epochs).
Why wrong: May overfit with small dataset.
Answer choices
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that 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.
Detailed technical explanation
Azure Custom Vision uses transfer learning with a pre-trained ResNet or similar architecture, fine-tuned on the provided images. The model's feature extractor is frozen initially, and only the final classification layers are trained; with very few images, the gradient updates are dominated by noise, causing the model to fail to learn robust features. In practice, a minimum of 50 images per class is recommended by Microsoft documentation, and adding variations (e.g., different orientations, lighting conditions) directly improves the model's ability to handle real-world defect detection scenarios.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Implement image and video processing solutions — This question tests Implement image and video processing solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Add at least 50 more images per class with variations. — Option B is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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