A data scientist is building a model to predict house prices from square footage. The scatter plot shows a clear upward trend but with increasing variance as square footage grows. The scientist fits a linear regression and obtains an R-squared of 0.85, but the residual plot shows a funnel shape. What should the scientist do next?

Apply a log transformation to the target variable and refit the linear model.

Remove outliers from the dataset and refit the model.

Use ridge regression to reduce the impact of multicollinearity.

Add polynomial terms to the model to capture the nonlinearity.

A team is fitting a polynomial regression model of degree 10 to a small dataset. The training error is near zero, but the validation error is high. Which action is most appropriate to improve generalization?

Increase the polynomial degree to 15

Use only the training error to evaluate the model

Reduce the polynomial degree and apply ridge regularization

How does polynomial regression extend linear regression to model non-linear relationships?

By replacing the linear equation with a logarithmic function of the features

By adding polynomial terms of the original features as new features in a linear model

By applying a non-linear transformation to the dependent variable before fitting a linear model

By using a non-linear optimization algorithm to fit the model parameters

A data scientist is using lasso regression for feature selection. The dataset has several highly correlated features. The scientist notices that the selected features vary significantly when the training data is slightly changed. What is the most likely cause and the best remedy?

The model is overfitting; use ridge regression

Lasso is unstable with correlated features; use elastic net instead

The data is not standardized; standardize the features

The alpha value is too low; increase alpha

GIAC Machine Learning Engineer

Domain 2Objective 2

Regressions GMLE Practice Questions (Page 5)

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 21–25

21application · medium
A data scientist is building a model to predict house prices from square footage. The scatter plot shows a clear upward trend but with increasing variance as square footage grows. The scientist fits a linear regression and obtains an R-squared of 0.85, but the residual plot shows a funnel shape. What should the scientist do next?
Select an answer first
22application · medium
A team is fitting a polynomial regression model of degree 10 to a small dataset. The training error is near zero, but the validation error is high. Which action is most appropriate to improve generalization?
Select an answer first
23foundation · easy
How does polynomial regression extend linear regression to model non-linear relationships?
Select an answer first
24expert · hard
A data scientist is using lasso regression for feature selection. The dataset has several highly correlated features. The scientist notices that the selected features vary significantly when the training data is slightly changed. What is the most likely cause and the best remedy?
Select an answer first
25application · medium
A linear regression model is fitted to predict crop yield from temperature. The residual plot shows a clear curved pattern. Which assumption of linear regression is most likely violated?
Select an answer first

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Regressions GMLE Practice Questions (Page 5)

Questions 21–25

A team is fitting a polynomial regression model of degree 10 to a small dataset. The training error is near zero, but the validation error is high. Which action is most appropriate to improve generalization?

How does polynomial regression extend linear regression to model non-linear relationships?

A data scientist is using lasso regression for feature selection. The dataset has several highly correlated features. The scientist notices that the selected features vary significantly when the training data is slightly changed. What is the most likely cause and the best remedy?

A linear regression model is fitted to predict crop yield from temperature. The residual plot shows a clear curved pattern. Which assumption of linear regression is most likely violated?

Keep studying — Machine Learning Algorithms