An e-commerce company wants to predict which customers are likely to make a purchase in the next month. The target variable is a binary outcome: 'purchase' or 'no purchase'. Which algorithm and evaluation metric are most appropriate?

K-means clustering with silhouette score

Logistic regression with area under the ROC curve (AUC)

Linear regression with mean squared error

A hospital develops a model to screen for a rare disease that affects 1% of the population. The model's predictions are used to decide which patients receive a confirmatory test. The cost of a false negative is extremely high (missed diagnosis), while false positives only cause additional testing. The model currently achieves 90% recall and 30% precision. Which change is most aligned with the clinical goal?

Use accuracy as the metric to evaluate the model

Lower the classification threshold to increase recall further

Raise the classification threshold to increase precision

Switch to a regression model to predict disease probability

A logistics company wants to estimate the delivery time (in hours) for a package based on distance, weather, and traffic conditions. Which combination of model type and evaluation metric is correct?

Linear regression with mean squared error

Logistic regression with accuracy

Decision tree classifier with F1-score

Support vector machine with precision

Which scenario is best suited for a regression task?

Predicting the exact temperature tomorrow in degrees Celsius

Grouping customers into segments based on purchasing behavior

Determining whether a loan applicant will default (yes/no)

Classifying images as containing a cat or a dog

A company wants to predict the number of products a customer will buy in the next quarter. The target variable is a count that can be 0, 1, 2, or more. The data is heavily skewed with many zeros. Which approach is most appropriate?

Use logistic regression to predict whether the count is zero or positive

Use a decision tree classifier with classes for each count value

Use linear regression and round predictions to the nearest integer

Use a regression model that can handle count data, such as Poisson regression

GIAC Machine Learning Engineer

Domain 2Objective 3

Supervised Learning GMLE Practice Questions (Page 2)

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 40 practice questions to prepare you well beyond it. (estimate)

40questions here

8free pages

6concepts

Questions 6–10

6application · easy
An e-commerce company wants to predict which customers are likely to make a purchase in the next month. The target variable is a binary outcome: 'purchase' or 'no purchase'. Which algorithm and evaluation metric are most appropriate?
Select an answer first
7application · medium
A hospital develops a model to screen for a rare disease that affects 1% of the population. The model's predictions are used to decide which patients receive a confirmatory test. The cost of a false negative is extremely high (missed diagnosis), while false positives only cause additional testing. The model currently achieves 90% recall and 30% precision. Which change is most aligned with the clinical goal?
Select an answer first
8application · medium
A logistics company wants to estimate the delivery time (in hours) for a package based on distance, weather, and traffic conditions. Which combination of model type and evaluation metric is correct?
Select an answer first
9foundation · easy
Which scenario is best suited for a regression task?
Select an answer first
10expert · hard
A company wants to predict the number of products a customer will buy in the next quarter. The target variable is a count that can be 0, 1, 2, or more. The data is heavily skewed with many zeros. Which approach is most appropriate?
Select an answer first

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