Question 796 of 754
How to Address Overfitting: Regularization as the First Step
A data scientist splits a dataset into training (80%) and test (20%). After training, the model achieves 95% accuracy on training and 60% on test. Which step should the data scientist take first?
Quick Answer
The correct first step is to apply regularization. This is because the model’s 95% training accuracy versus 60% test accuracy is a textbook symptom of overfitting, where the model has memorized noise in the training data rather than learning generalizable patterns. Regularization techniques, such as L1 (Lasso) or L2 (Ridge), directly counter this by adding a penalty term to the loss function, which discourages the model from assigning excessive weight to complex, non-generalizing features. On the CompTIA AI+ AI0-001 exam, this scenario tests your ability to diagnose overfitting and prioritize the most direct fix—regularization—before considering data augmentation or model simplification. A common trap is to immediately add more data, but the exam emphasizes that regularization is the first-line defense because it adjusts the model’s complexity without requiring new resources. Memory tip: think of regularization as a “complexity tax” that forces the model to pay for overconfidence, keeping it honest on unseen data.
⚠ Common exam trap
CompTIA often tests the misconception that overfitting is always solved by more data or cross-validation, but the immediate corrective action is to apply regularization to penalize 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 regularization
The model shows high training accuracy (95%) but significantly lower test accuracy (60%), which is a classic sign of overfitting. Regularization (Option C) directly addresses overfitting by adding a penalty term to the loss function (e.g., L1 or L2 regularization), discouraging the model from learning overly complex patterns that do not generalize. This is the first step because it targets the core issue without requiring additional data or increasing complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more data
Why it's wrong here
More data can help but is not the first step; regularization is quicker.
- ✗
Use cross-validation
Why it's wrong here
Cross-validation helps evaluate but does not fix overfitting.
- ✓
Apply regularization
Why this is correct
Regularization penalizes large weights, reducing overfitting.
- ✗
Increase model complexity
Why it's wrong here
This would likely worsen overfitting.
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Same concept, more angles
4 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An AI model achieves high accuracy on training data but performs poorly on new test data. The data scientist suspects the model has memorized noise. Which technique directly adds a penalty term to the loss function to address this?
hard- A.Batch normalization
- B.Data augmentation
- C.Dropout
- ✓ D.L2 regularization
Why D: L2 regularization (also known as weight decay) directly adds a penalty term proportional to the squared magnitude of the model's weights to the loss function. This discourages the model from fitting the noise in the training data by keeping weights small, thereby reducing overfitting and improving generalization to new test data.
Variation 2. Refer to the exhibit. The training log shows loss and accuracy for a binary classification model. What is the most likely issue with this model?
medium- ✓ A.Overfitting
- B.Insufficient epochs
- C.Underfitting
- D.Data leakage
Why A: The training log shows that training loss continues to decrease while validation loss increases after a certain point, and training accuracy approaches 100% while validation accuracy plateaus or drops. This divergence is the classic signature of overfitting, where the model memorizes noise in the training data rather than learning generalizable patterns.
Variation 3. Refer to the exhibit. The data scientist notices that the model achieves 98% accuracy on the training set but only 72% on the test set. Which change to the model parameters is most likely to reduce this gap?
easy- A.Increase n_estimators to 500.
- B.Set max_depth to None to allow trees to grow fully.
- ✓ C.Reduce max_depth to 3.
- D.Switch from RandomForest to a linear model like LogisticRegression.
Why C: The model is overfitting: 98% training accuracy vs. 72% test accuracy. Reducing max_depth to 3 limits the depth of each decision tree, preventing them from memorizing noise and forcing them to learn more generalizable patterns. This is a standard regularization technique for tree-based ensembles.
Variation 4. An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?
hard- A.Increase data augmentation
- B.Decrease the learning rate
- C.Increase the number of layers
- ✓ D.Add dropout layers
Why D: The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.
Last reviewed: Jun 30, 2026
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