In a simple linear regression model y = β0 + β1x + ε, what does the coefficient β1 represent?

The proportion of variance in y explained by x

The correlation coefficient between x and y

The expected change in y for a one-unit increase in x, holding all else constant

A data scientist is comparing two regression models. Model A has an R-squared of 0.90 and an adjusted R-squared of 0.85. Model B has an R-squared of 0.88 and an adjusted R-squared of 0.87. Which model should be preferred?

Model A, because higher R-squared always means better fit.

Model B, because the higher adjusted R-squared suggests better generalization.

Model A, because the difference between R-squared and adjusted R-squared is larger.

Model B, because lower R-squared is always better.

A data scientist is using ridge regression with alpha=0. What effect does this have compared to ordinary least squares (OLS)?

The model will overfit more than OLS

The model has more regularization

The model will have zero coefficients

A data scientist is fitting a polynomial regression model. They notice that the coefficients are very large and the model performs poorly on the validation set. What is the most likely cause and the best remedy?

Multicollinearity; use a different model.

The data is not scaled; scale the features.

Overfitting due to high-degree polynomial; apply regularization or reduce the degree.

Underfitting due to low-degree polynomial; increase the degree.

GIAC Machine Learning Engineer

Domain 2Objective 2

Regressions GMLE Practice Questions (Page 1)

Part of the Machine Learning Algorithms domain, which makes up ~29% of our current practice bank. GIAC (SANS) does not publish an official question count, but from its 180-minute exam (~70–120 total, ~20–35 in this domain), expect 7–12 from this objective — we provide 36 practice questions to prepare you well beyond it. (estimate)

36questions here

8free pages

5concepts

Questions 1–5

1foundation · easy
Which regression evaluation metric is expressed in the same units as the target variable and penalizes large errors more heavily than MAE?
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2foundation · easy
In a simple linear regression model y = β0 + β1x + ε, what does the coefficient β1 represent?
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3expert · hard
A data scientist is comparing two regression models. Model A has an R-squared of 0.90 and an adjusted R-squared of 0.85. Model B has an R-squared of 0.88 and an adjusted R-squared of 0.87. Which model should be preferred?
Select an answer first
4application · medium
A data scientist is using ridge regression with alpha=0. What effect does this have compared to ordinary least squares (OLS)?
Select an answer first
5expert · hard
A data scientist is fitting a polynomial regression model. They notice that the coefficients are very large and the model performs poorly on the validation set. What is the most likely cause and the best remedy?
Select an answer first

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