Quick Answer
The correct choice is to apply Ridge regularization, because this L2 technique directly reduces variance by adding a penalty term proportional to the square of the model’s coefficients, shrinking them toward zero without eliminating any features. This constraint on coefficient size limits model complexity, which is the root cause of high variance and overfitting, while preserving the low bias that indicates the model already fits the training data well. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of the bias-variance trade-off and regularization methods; a common trap is confusing Ridge with Lasso, which uses absolute value penalties and can zero out coefficients entirely. To remember, think of Ridge as “riding” the coefficients down smoothly—it shrinks but never drops them.
This DA0-001 practice question tests your understanding of analyzing and modeling data. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?
Apply Ridge regularization
Ridge adds penalty to coefficients, reducing overfitting and variance.
Increase polynomial features
Why wrong: This would likely increase variance further.
Use a smaller training set
Why wrong: Smaller training set typically increases variance.
Remove correlated features
Why wrong: This may reduce variance but can increase bias; not the most direct method.
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 Ridge regularization
Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.
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.
Apply Ridge regularization
Why this is correct
Ridge adds penalty to coefficients, reducing overfitting and variance.
Related concept
Read the scenario before looking for a memorised answer.
Increase polynomial features
Why it's wrong here
This would likely increase variance further.
Use a smaller training set
Why it's wrong here
Smaller training set typically increases variance.
Remove correlated features
Why it's wrong here
This may reduce variance but can increase bias; not the most direct method.
Common exam traps
CompTIA often tests the misconception that reducing variance requires removing features or simplifying the model, but Ridge regularization is the correct technique because it penalizes coefficient magnitude without discarding predictors.
Detailed technical explanation
Ridge regression modifies the ordinary least squares objective by adding λ * Σ(β_j²) to the loss function, which introduces a bias but stabilizes coefficient estimates, especially when features are correlated. Under the hood, this is equivalent to imposing a Gaussian prior on the coefficients, shrinking them proportionally to their variance. In real-world scenarios like genomic data with thousands of correlated features, Ridge is preferred over Lasso when all features are expected to contribute, as it retains all predictors while controlling overfitting.
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.
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Apply Ridge regularization — Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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