- A
Learning rate scheduling
Why wrong: Learning rate scheduling adjusts the learning rate over time, which helps convergence but not overfitting directly.
- B
Batch normalization
Why wrong: Batch normalization standardizes layer inputs and can speed up training but is not primarily for overfitting.
- C
Dropout
Correct: Dropout is specifically designed to reduce overfitting in large neural networks.
- D
Early stopping
Why wrong: Early stopping halts training when validation loss increases, which can prevent overfitting, but dropout is more effective for very large models.
Question 839 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 model for sentiment analysis has millions of parameters and is trained on a small dataset. Which technique can help prevent overfitting?
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
✓
Dropout
Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This is particularly effective when the model has millions of parameters but is trained on a small dataset, as it reduces co-adaptation of neurons and mitigates overfitting.
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.
- ✗
Learning rate scheduling
Why it's wrong here
Learning rate scheduling adjusts the learning rate over time, which helps convergence but not overfitting directly.
- ✗
Batch normalization
Why it's wrong here
Batch normalization standardizes layer inputs and can speed up training but is not primarily for overfitting.
- ✓
Dropout
Why this is correct
Correct: Dropout is specifically designed to reduce overfitting in large neural networks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Early stopping
Why it's wrong here
Early stopping halts training when validation loss increases, which can prevent overfitting, but dropout is more effective for very large models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that batch normalization or learning rate scheduling are regularization techniques, when in fact they address optimization and training stability, not the fundamental overfitting problem caused by a high parameter count relative to dataset size.
Detailed technical explanation
How to think about this question
Dropout works by applying a Bernoulli mask to each layer's activations during training, effectively training an ensemble of sub-networks, and at test time scales weights by the keep probability (e.g., 0.5) to approximate averaging. In practice, dropout is often used with a rate of 0.2–0.5 for fully connected layers, and its effectiveness diminishes with very large datasets where overfitting is less of a concern. A subtle behavior is that dropout can increase training time because the model must learn to perform well with missing neurons, but it often yields better generalization.
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: Dropout — Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This is particularly effective when the model has millions of parameters but is trained on a small dataset, as it reduces co-adaptation of neurons and mitigates overfitting.
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
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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