- A
Reduce the image dimensions to speed up training.
Why wrong: Reducing size may discard important details for medical images.
- B
Add more negative samples to the dataset.
Why wrong: Negative samples help but do not substitute for more positive samples.
- C
Use data augmentation and transfer learning with a pre-trained model.
Data augmentation increases effective dataset size, and transfer learning leverages pre-trained features.
- D
Increase the number of training iterations significantly.
Why wrong: More iterations without more data can cause overfitting.
Question 599 of 993
Improve Custom Vision Model Accuracy with Small Datasets
This AI-102 practice question tests your understanding of implement computer vision solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 healthcare organization uses Custom Vision to classify X-ray images. They have a small dataset of 200 images per class. Which strategy will most likely improve model accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Use data augmentation and transfer learning with a pre-trained model.
Option C is correct because data augmentation artificially expands the small dataset (200 images per class) by applying transformations like rotation, scaling, and flipping, which helps the model generalize better. Transfer learning with a pre-trained model (e.g., ResNet or EfficientNet) leverages features learned from large datasets like ImageNet, allowing the Custom Vision model to achieve higher accuracy with limited data.
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.
- ✗
Reduce the image dimensions to speed up training.
Why it's wrong here
Reducing size may discard important details for medical images.
- ✗
Add more negative samples to the dataset.
Why it's wrong here
Negative samples help but do not substitute for more positive samples.
- ✓
Use data augmentation and transfer learning with a pre-trained model.
Why this is correct
Data augmentation increases effective dataset size, and transfer learning leverages pre-trained features.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of training iterations significantly.
Why it's wrong here
More iterations without more data can cause overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume more iterations (Option D) always improve accuracy, but in small datasets, this leads to overfitting, while data augmentation and transfer learning directly address the root cause of limited data.
Detailed technical explanation
How to think about this question
Under the hood, Custom Vision uses a deep neural network (e.g., a variant of ResNet) that is pre-trained on ImageNet; transfer learning fine-tunes the final layers on the X-ray dataset, while data augmentation (e.g., random crops, color jitter) acts as a regularizer to reduce overfitting. In real-world medical imaging scenarios, a dataset of 200 images per class is considered small, and without augmentation, the model may fail to generalize to variations in patient positioning or X-ray machine settings. The Azure Custom Vision service automatically applies basic augmentation, but manual augmentation via the training API or SDK can further boost performance.
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 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
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: Use data augmentation and transfer learning with a pre-trained model. — Option C is correct because data augmentation artificially expands the small dataset (200 images per class) by applying transformations like rotation, scaling, and flipping, which helps the model generalize better. Transfer learning with a pre-trained model (e.g., ResNet or EfficientNet) leverages features learned from large datasets like ImageNet, allowing the Custom Vision model to achieve higher accuracy with limited data.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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