- A
Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).
Regularization and feature selection reduce overfitting by penalizing large coefficients and removing redundant predictors.
- B
Add more predictor variables to increase the training R-squared further.
Why wrong: Adding more predictors will likely worsen overfitting, not fix it.
- C
Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.
Why wrong: Cross-validation changes evaluation but does not directly fix overfitting; the model itself needs adjustment.
- D
Increase the number of hidden layers in the model to capture more complexity.
Why wrong: Linear regression does not have hidden layers; this is irrelevant for a linear model.
Quick Answer
The correct first action is to remove highly correlated predictor variables and apply regularization such as Ridge or Lasso regression. This discrepancy—an R-squared of 0.99 on training data versus only 0.55 on the test set—is a textbook symptom of overfitting, where the model has memorized noise and specific patterns in the training data rather than learning generalizable relationships. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of model generalization and the bias-variance tradeoff; a common trap is to assume that adding more predictors always improves the model, when in fact too many features, especially correlated ones, inflate variance. Regularization techniques like Ridge (L2) and Lasso (L1) shrink coefficient magnitudes, directly penalizing complexity to improve out-of-sample performance. To remember this, think of the mnemonic “CRR” for Correlated features, Regularization, and Reduced overfitting—the three-step fix for a model that’s too complex.
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.
A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?
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.
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
Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).
The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.
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.
- ✓
Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).
Why this is correct
Regularization and feature selection reduce overfitting by penalizing large coefficients and removing redundant predictors.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more predictor variables to increase the training R-squared further.
Why it's wrong here
Adding more predictors will likely worsen overfitting, not fix it.
- ✗
Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.
Why it's wrong here
Cross-validation changes evaluation but does not directly fix overfitting; the model itself needs adjustment.
- ✗
Increase the number of hidden layers in the model to capture more complexity.
Why it's wrong here
Linear regression does not have hidden layers; this is irrelevant for a linear model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think a high R-squared is always good, or they may confuse overfitting with underfitting and choose to add more complexity (Option D) or more data (Option B), rather than recognizing the need to reduce model complexity and apply regularization.
Detailed technical explanation
How to think about this question
Regularization techniques like Ridge (L2) add a penalty term λ∑β² to the loss function, shrinking coefficients toward zero but not eliminating them, which reduces variance at the cost of slight bias. Lasso (L1) adds λ∑|β|, which can drive some coefficients exactly to zero, performing automatic feature selection. In a dataset with 50 predictors, many may be redundant or noisy, and regularization helps the model focus on the most influential variables, improving out-of-sample performance.
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.
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FAQ
Questions learners often ask
What does this DA0-001 question test?
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: Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso). — The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 24, 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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