- A
Increasing training data size
More data helps the model generalize better.
- B
L2 regularization
L2 adds penalty on weights, discouraging overly complex models.
- C
Using a larger learning rate
Why wrong: Larger learning rate can cause divergence; not a regularization technique.
- D
Dropout
Dropout is a regularization technique that prevents co-adaptation.
- E
Increasing number of layers
Why wrong: More layers increase model capacity, worsening overfitting.
Question 674 of 1,000
3 Techniques to Reduce Overfitting in Neural Networks
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.
Which THREE techniques can help reduce overfitting in neural networks?
Quick Answer
The answer is dropout, L2 regularization, and increasing the size of the training dataset. These three techniques reduce overfitting in neural networks by targeting different causes of memorization: dropout randomly deactivates neurons during training to force the network to learn redundant representations, L2 regularization penalizes overly large weights to keep the model simpler, and more data provides a richer set of examples so the network generalizes rather than memorizes noise. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of practical model tuning—expect it in the “Model Optimization” domain, often with a distractor like “increasing learning rate” or “adding more layers,” which actually worsen overfitting. A common trap is confusing regularization with optimization; remember that reducing overfitting always involves adding constraints or data, not speeding up training. Memory tip: think “Drop, Penalize, Expand”—drop neurons, penalize weights, expand data.
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
✓
Increasing training data size
Increasing the training data size helps reduce overfitting by providing the model with more examples to learn from, which reduces the variance and improves generalization. With more data, the model is less likely to memorize noise and instead learns the underlying patterns, making it more robust on 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.
- ✓
Increasing training data size
Why this is correct
More data helps the model generalize better.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
L2 regularization
Why this is correct
L2 adds penalty on weights, discouraging overly complex models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Using a larger learning rate
Why it's wrong here
Larger learning rate can cause divergence; not a regularization technique.
- ✓
Dropout
Why this is correct
Dropout is a regularization technique that prevents co-adaptation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increasing number of layers
Why it's wrong here
More layers increase model capacity, worsening overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that increasing model complexity (e.g., more layers or larger learning rates) can help with overfitting, when in fact these changes typically worsen it by increasing variance or destabilizing training.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model learns the training data too well, including its noise, leading to poor performance on new data. Techniques like L2 regularization add a penalty proportional to the square of the weights to the loss function, encouraging smaller weights and simpler models. Dropout randomly deactivates a fraction of neurons during training, forcing the network to learn redundant representations and reducing co-adaptation, which acts as an ensemble method. In practice, combining data augmentation (which effectively increases training data size) with dropout and L2 regularization is a common strategy to combat overfitting in deep learning.
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
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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: Increasing training data size — Increasing the training data size helps reduce overfitting by providing the model with more examples to learn from, which reduces the variance and improves generalization. With more data, the model is less likely to memorize noise and instead learns the underlying patterns, making it more robust on 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.
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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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