The answer is Ridge regression (L2). This technique reduces overfitting in polynomial regression by adding a penalty proportional to the square of the coefficient magnitudes, which shrinks them toward zero without eliminating them entirely. By controlling the influence of higher-degree polynomial terms, Ridge regularization reduces variance and prevents the model from fitting noise in the training data, making it the ideal choice when you want to minimize overfitting while retaining all features. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of regularization methods—a common trap is confusing Ridge with Lasso (L1), which eliminates coefficients entirely. A helpful memory tip: think of Ridge as "squaring off" the coefficients—it shrinks them but keeps them in the game, just like a ridge on a mountain that stays connected to the ground.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An analyst is fitting a polynomial regression model and wants to choose the degree that minimizes overfitting. Which technique should the analyst use?
Why wrong: Lasso can reduce overfitting by shrinking some coefficients to zero, but it may eliminate useful features too aggressively for polynomial terms.
B
Principal component analysis (PCA)
Why wrong: PCA reduces dimensionality but does not control for overfitting due to large coefficients; it may discard important polynomial terms.
C
Stepwise selection
Why wrong: Stepwise selection selects features based on statistical criteria but does not explicitly address coefficient magnitude or overfitting.
D
Ridge regression (L2)
Ridge regression penalizes large coefficients, which is effective for reducing overfitting in polynomial models without removing features.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ridge regression (L2)
Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all features.
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.
✗
Lasso regression (L1)
Why it's wrong here
Lasso can reduce overfitting by shrinking some coefficients to zero, but it may eliminate useful features too aggressively for polynomial terms.
✗
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but does not control for overfitting due to large coefficients; it may discard important polynomial terms.
✗
Stepwise selection
Why it's wrong here
Stepwise selection selects features based on statistical criteria but does not explicitly address coefficient magnitude or overfitting.
✓
Ridge regression (L2)
Why this is correct
Ridge regression penalizes large coefficients, which is effective for reducing overfitting in polynomial models without removing features.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Lasso (L1) with Ridge (L2), mistakenly thinking Lasso's coefficient elimination is always better for overfitting, when in fact Ridge's smooth shrinkage is more appropriate for polynomial models where all degrees should be retained but controlled.
Detailed technical explanation
How to think about this question
Ridge regression modifies the ordinary least squares objective by adding a penalty term λ * Σ(β²), where λ controls the shrinkage strength. In polynomial regression, higher-degree terms often have large coefficients that cause overfitting; ridge regression shrinks these coefficients proportionally, reducing model complexity without discarding terms. A subtle behavior is that ridge regression assumes all features are on a similar scale, so standardizing predictors is critical to avoid biased penalization of larger-magnitude polynomial terms.
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 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
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: Ridge regression (L2) — Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all features.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This DA0-001 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 DA0-001 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.