The answer is B: apply transfer learning using a pre-trained model on ImageNet. This approach is the most effective because transfer learning for medical image classification allows the team to leverage features already learned from a massive, diverse dataset, which dramatically reduces training time and data requirements while improving generalization—especially critical when the dataset is small and imbalanced, as with 8,000 benign and 2,000 malignant lesions. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of when to use transfer learning versus architectural tweaks; a common trap is assuming more dropout or fewer layers will fix plateauing accuracy, but those only address overfitting after the model has decent features, not the core issue of insufficient learned representations. Remember the mnemonic “Pre-train, then fine-tune” to recall that transfer learning gives a head start on feature extraction, making it the go-to solution for limited data and GPU constraints.
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 startup is developing a diagnostic system using medical images. The team has collected 10,000 labeled images of skin lesions. They plan to train a convolutional neural network (CNN) from scratch. However, training converges slowly, and the validation accuracy plateaus at 70%. The data scientist suspects overfitting. The dataset contains 8,000 images of benign lesions and 2,000 of malignant. The team has limited GPU resources. Which of the following is the MOST effective course of action to improve validation accuracy? A. Reduce the number of convolutional layers. B. Apply transfer learning using a pre-trained model on ImageNet. C. Increase the learning rate by a factor of 10. D. Add more dropout after every convolutional layer.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply transfer learning using a pre-trained model on ImageNet.
Option B is correct. Transfer learning leverages a model pre-trained on a large dataset (e.g., ImageNet), which provides useful features for medical images and reduces the need for large amounts of data and computational resources. It is particularly effective when the dataset is small and imbalanced. Option A (reducing layers) may reduce capacity and underfit. Option C (increasing learning rate) might cause divergence or overshoot minima. Option D (adding dropout) can help with overfitting but is unlikely to jump from 70% to a significantly higher accuracy given limited data; transfer learning provides a stronger boost.
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 by a factor of 10.
Why it's wrong here
Increasing learning rate too much can cause the optimizer to overshoot and fail to converge.
✗
Reduce the number of convolutional layers.
Why it's wrong here
Reducing layers reduces model capacity, which may lead to underfitting and lower performance.
✗
Add more dropout after every convolutional layer.
Why it's wrong here
While dropout regularizes, it may not be sufficient to overcome overfitting from a small dataset; transfer learning is more impactful.
✓
Apply transfer learning using a pre-trained model on ImageNet.
Why this is correct
Transfer learning provides a strong feature extractor learned from a large dataset, which can significantly improve performance with limited data.
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
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which AI0-001 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.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply transfer learning using a pre-trained model on ImageNet. — Option B is correct. Transfer learning leverages a model pre-trained on a large dataset (e.g., ImageNet), which provides useful features for medical images and reduces the need for large amounts of data and computational resources. It is particularly effective when the dataset is small and imbalanced. Option A (reducing layers) may reduce capacity and underfit. Option C (increasing learning rate) might cause divergence or overshoot minima. Option D (adding dropout) can help with overfitting but is unlikely to jump from 70% to a significantly higher accuracy given limited data; transfer learning provides a stronger boost.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 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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