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.
This AI0-001 practice question tests your understanding of ai concepts and foundations. 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 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?
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.
Collect more data
Why wrong: More data can help but is not the first step; regularization is quicker.
Use cross-validation
Why wrong: Cross-validation helps evaluate but does not fix overfitting.
Apply regularization
Regularization penalizes large weights, reducing overfitting.
Increase model complexity
Why wrong: This would likely worsen overfitting.
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 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.
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.
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.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Increase model complexity
Why it's wrong here
This would likely worsen overfitting.
Common exam traps
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.
Detailed technical explanation
Regularization works by adding a term like λ∑w² (L2) or λ∑|w| (L1) to the loss function, which constrains the model weights during optimization. In practice, the regularization strength (λ) must be tuned via validation; too high a value can cause underfitting. For example, in logistic regression or neural networks, L2 regularization (also known as weight decay) is commonly applied to penalize large weights, effectively reducing variance without increasing bias significantly.
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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FAQ
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
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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Same concept, more angles
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?
hardWhy 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?
mediumWhy A: The training loss decreases and training accuracy increases, but validation loss increases and validation accuracy decreases. This is a classic sign of overfitting, where the model learns training data noise but fails to generalize. Underfitting would show both training and validation loss high. Data leakage would show unusually high accuracy early. Insufficient epochs would show both losses still decreasing.
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?
easyWhy 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?
hardWhy 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.
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Last reviewed: Jun 30, 2026
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