- A
Add dropout layers
Dropout randomly drops units during training, reducing co-adaptation.
- B
Apply L2 regularization
L2 penalizes large weights, preventing overfitting.
- C
Use data augmentation
Augmentation increases effective training set size, improving generalization.
- D
Use a smaller learning rate
Why wrong: Smaller learning rate may slow convergence but does not directly regularize the model.
- E
Increase the number of convolutional layers
Why wrong: More layers increase model complexity and typically worsen overfitting.
Question 902 of 1,000
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 deep learning engineer is training a convolutional neural network for image classification. The model is overfitting the training data. Which three techniques can help reduce overfitting? (Choose three.)
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 dropout layers
Option A is correct because dropout layers randomly deactivate a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features. This reduces overfitting by acting as a form of ensemble learning without increasing model complexity.
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.
- ✓
Add dropout layers
Why this is correct
Dropout randomly drops units during training, reducing co-adaptation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Apply L2 regularization
Why this is correct
L2 penalizes large weights, preventing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use data augmentation
Why this is correct
Augmentation increases effective training set size, improving generalization.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a smaller learning rate
Why it's wrong here
Smaller learning rate may slow convergence but does not directly regularize the model.
- ✗
Increase the number of convolutional layers
Why it's wrong here
More layers increase model complexity and typically worsen overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In CompTIA AI+ exams, a common trap is assuming that reducing the learning rate or increasing model depth can mitigate overfitting, when in fact these adjustments either have no effect on overfitting or worsen it.
Detailed technical explanation
How to think about this question
Dropout works by sampling a sub-network from the full network at each training step, effectively training an ensemble of models with shared weights. During inference, dropout is turned off and weights are scaled by the keep probability (e.g., 0.5) to approximate the ensemble's output. L2 regularization adds a penalty proportional to the square of the weight magnitudes to the loss function, encouraging smaller weights and smoother decision boundaries. Data augmentation generates new training samples through transformations like rotation, flipping, and cropping, increasing the effective dataset size and reducing the model's sensitivity to irrelevant variations.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
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: Add dropout layers — Option A is correct because dropout layers randomly deactivate a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features. This reduces overfitting by acting as a form of ensemble learning without increasing model complexity.
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 →
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Last reviewed: Jul 4, 2026
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