AI0-001 AI Models and Data Engineering Practice Question
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?
⚠ Common exam trap
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.
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.
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.
- ✗
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.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.