- A
Use data augmentation
Data augmentation generates variations of training images, effectively increasing the dataset size and reducing overfitting.
- B
Increase the number of epochs
Why wrong: More epochs can lead to overfitting without other regularization.
- C
Use L2 regularization
Why wrong: L2 regularization helps but is generally less effective for CNNs compared to dropout and augmentation.
- D
Add more convolutional layers
Why wrong: Adding more layers increases model capacity, which can worsen overfitting.
- E
Use dropout layers
Dropout is a regularization technique that prevents overfitting by randomly deactivating neurons.
Question 627 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. 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 data scientist is preparing to train a convolutional neural network (CNN) for image classification. Which TWO actions are most effective for preventing overfitting? (Choose 2)
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
Data augmentation (A) is effective for preventing overfitting because it artificially expands the training dataset by applying random transformations (e.g., rotation, flipping, cropping, color jitter) to existing images. This exposes the CNN to a wider variety of input patterns, reducing the model's tendency to memorize noise or specific details and improving generalization to unseen 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.
- ✓
Use data augmentation
Why this is correct
Data augmentation generates variations of training images, effectively increasing the dataset size and reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs can lead to overfitting without other regularization.
- ✗
Use L2 regularization
Why it's wrong here
L2 regularization helps but is generally less effective for CNNs compared to dropout and augmentation.
- ✗
Add more convolutional layers
Why it's wrong here
Adding more layers increases model capacity, which can worsen overfitting.
- ✓
Use dropout layers
Why this is correct
Dropout is a regularization technique that prevents overfitting by randomly deactivating neurons.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between regularization techniques that directly reduce overfitting (like dropout and data augmentation) versus architectural changes (like adding layers) that increase capacity and may worsen overfitting if not balanced with regularization.
Detailed technical explanation
How to think about this question
Dropout layers (E) work by randomly deactivating a fraction of neurons during each training iteration, forcing the network to learn redundant representations and preventing co-adaptation of features. In CNNs, dropout is often applied after fully connected layers, but spatial dropout (dropping entire feature maps) is also used for convolutional layers. Data augmentation, when combined with batch normalization and dropout, forms a robust defense against overfitting in deep image classifiers, especially when the original dataset is small.
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 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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use data augmentation — Data augmentation (A) is effective for preventing overfitting because it artificially expands the training dataset by applying random transformations (e.g., rotation, flipping, cropping, color jitter) to existing images. This exposes the CNN to a wider variety of input patterns, reducing the model's tendency to memorize noise or specific details and improving generalization to unseen data.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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