- A
Increase the number of hidden layers
Why wrong: More layers increase model complexity, likely worsening overfitting.
- B
Apply L2 regularization
Regularization adds a penalty on large weights, reducing overfitting and improving generalization.
- C
Increase the learning rate
Why wrong: Higher learning rate may cause unstable training and does not directly address overfitting.
- D
Add more training data
Why wrong: More data helps but is not the most direct or immediate fix; regularization is more targeted.
Question 192 of 1,000
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 machine learning team is developing a model to predict server failure from telemetry data. They use a deep neural network with 3 hidden layers. After training, the model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the team apply to reduce the generalization error?
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
✓
Apply L2 regularization
The model exhibits high variance (overfitting) because it achieves 99% accuracy on training data but only 85% on validation data. L2 regularization (also known as weight decay) adds a penalty proportional to the squared magnitude of the weights to the loss function, which discourages the network from fitting noise in the training data and improves generalization. This directly reduces the gap between training and validation 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 number of hidden layers
Why it's wrong here
More layers increase model complexity, likely worsening overfitting.
- ✓
Apply L2 regularization
Why this is correct
Regularization adds a penalty on large weights, reducing overfitting and improving generalization.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate may cause unstable training and does not directly address overfitting.
- ✗
Add more training data
Why it's wrong here
More data helps but is not the most direct or immediate fix; regularization is more targeted.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between techniques that address overfitting (regularization) versus those that address underfitting (more layers, higher learning rate) or data quantity, leading candidates to mistakenly choose adding more data or increasing model complexity.
Detailed technical explanation
How to think about this question
L2 regularization modifies the loss function by adding λ * Σ(w_i²), where λ is the regularization strength. During backpropagation, this creates a gradient that shrinks weights toward zero, effectively reducing the model's reliance on any single feature and smoothing the decision boundary. In practice, a common default λ value is 0.001, and tuning it via cross-validation is critical because too high a value can cause underfitting.
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?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply L2 regularization — The model exhibits high variance (overfitting) because it achieves 99% accuracy on training data but only 85% on validation data. L2 regularization (also known as weight decay) adds a penalty proportional to the squared magnitude of the weights to the loss function, which discourages the network from fitting noise in the training data and improves generalization. This directly reduces the gap between training and validation 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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