AI0-001 AI Implementation and Operations Practice Question
A data scientist trains a regression model and notices the training loss is low but validation loss is high. Which technique should be applied FIRST to address this issue?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and underfitting, and the trap here is that candidates may incorrectly choose to increase dataset size (Option C) as the first action, when regularization is the more immediate and practical first step to address overfitting without requiring new data collection.
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 L1 or L2 regularization to the model.
The scenario describes overfitting, where the model memorizes the training data but fails to generalize to unseen data. Applying L1 or L2 regularization (Option D) is the correct first step because it adds a penalty to the loss function for large weights, discouraging complexity and reducing overfitting without requiring additional data or architectural changes.
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 learning rate.
Why it's wrong here
Learning rate affects training speed, not generalization.
- ✗
Add more layers to the neural network.
Why it's wrong here
Adding layers increases capacity, likely worsening overfitting.
- ✗
Increase the size of the training dataset.
Why it's wrong here
More data can help but is not always the first action.
- ✓
Apply L1 or L2 regularization to the model.
Why this is correct
Regularization penalizes large weights, reducing overfitting.
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