Quick Answer
The correct answer is to apply L2 regularization to the network weights. This technique directly addresses overfitting, which is the core issue when a model achieves 99% accuracy on training data but only 85% on validation data—a classic sign that the network has memorized noise rather than learning generalizable patterns. L2 regularization works by adding a penalty term proportional to the squared magnitude of the weights to the loss function, which forces the model to keep weights small and prevents it from fitting overly complex, non-generalizable features. On the CompTIA AI+ AI0-001 exam, this scenario tests your ability to recognize overfitting and select the first-line remedy; a common trap is to choose dropout or data augmentation first, but L2 regularization is often the simplest initial fix because it doesn’t require restructuring the network. Remember the memory tip: “L2 squares the weights to flatten the overfit.”
This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is training a neural network to classify images of handwritten digits. The model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the scientist apply first to address this issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Remove one or more hidden layers from the network
Why wrong: Removing layers may cause underfitting.
Increase the number of training epochs
Why wrong: Increasing epochs typically increases overfitting.
Apply L2 regularization to the network weights
L2 regularization penalizes large weights and reduces overfitting.
Add more features to the input data
Why wrong: Adding features is not a primary remedy for overfitting in image classification.
Answer choices
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 to the network weights
The model shows high training accuracy (99%) but lower validation accuracy (85%), which is a classic sign of overfitting. L2 regularization (option C) adds a penalty term to the loss function proportional to the squared magnitude of the weights, discouraging the network from learning overly complex patterns that do not generalize. This directly addresses overfitting without reducing the model's capacity too aggressively.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Remove one or more hidden layers from the network
Why it's wrong here
Removing layers may cause underfitting.
Increase the number of training epochs
Why it's wrong here
Increasing epochs typically increases overfitting.
Apply L2 regularization to the network weights
Why this is correct
L2 regularization penalizes large weights and reduces overfitting.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Add more features to the input data
Why it's wrong here
Adding features is not a primary remedy for overfitting in image classification.
Common exam traps
CompTIA often tests the distinction between overfitting and underfitting, and the trap here is that candidates may confuse increasing epochs (option B) as a solution to low validation accuracy, when in fact it exacerbates overfitting in this scenario.
Detailed technical explanation
L2 regularization (also known as weight decay) modifies the gradient update by subtracting a fraction of the weight value at each step, effectively penalizing large weights. In practice, the regularization hyperparameter λ controls the trade-off between fitting the training data and keeping weights small; a common starting value is 0.01 or 0.001. This technique is particularly effective in deep networks where overfitting arises from high-capacity models, and it is often combined with dropout for robust generalization.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
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Machine Learning and Deep Learning — study guide chapter
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FAQ
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Apply L2 regularization to the network weights — The model shows high training accuracy (99%) but lower validation accuracy (85%), which is a classic sign of overfitting. L2 regularization (option C) adds a penalty term to the loss function proportional to the squared magnitude of the weights, discouraging the network from learning overly complex patterns that do not generalize. This directly addresses overfitting without reducing the model's capacity too aggressively.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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