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.
Refer to the exhibit. An AI developer implements the above neural network architecture for handwritten digit recognition. The model achieves 85% training accuracy and 83% test accuracy. Which modification is most likely to improve training accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
The answer is to remove the dropout layer. Dropout is a regularization technique that randomly deactivates neurons during training to prevent overfitting, but when training accuracy is already low—85% in this case—it can actually hinder the model’s ability to learn the training data fully. By removing dropout, you reduce regularization, allowing the neural network to fit the training set more closely and directly increase training accuracy. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of the trade-off between regularization and underfitting; a common trap is assuming dropout always improves performance, when in fact it can suppress accuracy if the model is not yet overfitting. A useful memory tip: “Dropout drops accuracy when training is weak—remove it to let the network speak.”
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
✓
Remove the dropout layer
The model achieves 85% training accuracy and 83% test accuracy, indicating slight overfitting (training accuracy is higher than test accuracy). Removing the dropout layer reduces regularization, allowing the model to fit the training data more closely, which directly improves training accuracy. Dropout randomly drops neurons during training to prevent overfitting, but if the model is already underfitting or the gap is small, removing it can boost training performance.
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 dropout rate to 0.7
Why it's wrong here
Increasing dropout adds more regularization, which would likely decrease training accuracy.
✗
Increase the number of filters in the first Conv2D layer
Why it's wrong here
More filters increase capacity but may not directly improve training accuracy; could lead to overfitting.
✗
Add another dense layer before the output
Why it's wrong here
Adding layers increases complexity but may not significantly boost training accuracy if current capacity is sufficient.
✓
Remove the dropout layer
Why this is correct
Dropout adds regularization; removing it can increase training accuracy, especially if the model is underfitting.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA AI often tests the misconception that dropout always improves accuracy, when in fact removing dropout can be the correct modification to boost training accuracy if the model is not overfitting significantly.
Detailed technical explanation
How to think about this question
Dropout is a regularization technique that randomly sets a fraction of input units to 0 at each update during training, which prevents co-adaptation of neurons. In this scenario, the small gap between training and test accuracy (2%) suggests mild overfitting, so removing dropout allows the network to fully utilize all neurons for the training set, directly increasing training accuracy. In practice, dropout is often tuned as a hyperparameter; a rate of 0.5 is common, and removing it entirely is a valid strategy when the model is underperforming on training data.
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.
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: Remove the dropout layer — The model achieves 85% training accuracy and 83% test accuracy, indicating slight overfitting (training accuracy is higher than test accuracy). Removing the dropout layer reduces regularization, allowing the model to fit the training data more closely, which directly improves training accuracy. Dropout randomly drops neurons during training to prevent overfitting, but if the model is already underfitting or the gap is small, removing it can boost training performance.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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