The correct answer is to apply L2 regularization, commonly known as Ridge regression, because the analyst’s model is clearly overfitting—it memorizes the training data but fails to generalize to new validation data. Ridge regression addresses this by adding a penalty term proportional to the square of the coefficient magnitudes, which forces the model to shrink less important features toward zero without eliminating them entirely, thereby reducing variance and improving out-of-sample performance. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of regularization as a bias-variance tradeoff tool; a common trap is confusing Ridge with Lasso (L1), which can zero out coefficients entirely. Remember that Ridge “rides” the squared penalty to keep all features in play while smoothing the model. For a quick memory tip: think of the “R” in Ridge as standing for “round” (squared penalty) and “retain” (keeps all variables).
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Ridge regression modifies the ordinary least squares objective by adding a regularization term λ * Σ(β²), where λ controls the strength of the penalty. This shrinks coefficients toward zero but does not set them exactly to zero, preserving all features while reducing their influence. In practice, choosing λ via cross-validation is critical, as too high a value can underfit while too low fails to mitigate overfitting.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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