DA0-002 Data Analysis Practice Question
A data analyst is working with a dataset containing house prices. After building a multiple linear regression model, the analyst observes that the model performs well on training data but poorly on validation data. Which technique is most appropriate to address this issue?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and underfitting, and candidates mistakenly choose polynomial transformation or adding features thinking they will improve fit, when in fact they increase model complexity and worsen overfitting.
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 (Ridge)
The model is overfitting the training data, as evidenced by high performance on training data but poor performance on validation data. L2 regularization (Ridge) adds a penalty term proportional to the square of the coefficients, which shrinks them and reduces model complexity, thereby improving generalization to unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the training data size
Why it's wrong here
Reducing training data typically increases variance and overfitting.
- ✗
Use a polynomial transformation
Why it's wrong here
Polynomial transformations increase model complexity, likely worsening overfitting.
- ✗
Increase the number of features
Why it's wrong here
Adding more features increases model complexity and overfitting.
- ✓
Apply L2 regularization (Ridge)
Why this is correct
Ridge regularization adds a penalty to large coefficients, reducing variance and combating overfitting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 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 DA0-002 exam.