A logistics company wants to predict the delivery time (in hours) for packages based on distance, traffic conditions, and weather. The target variable is a continuous value. Which algorithm and evaluation metric are most appropriate?

K-means clustering with silhouette score

Logistic regression with accuracy

Support vector regression with mean absolute error (MAE)

Decision tree classifier with F1-score

Which statement best distinguishes supervised learning from reinforcement learning?

Supervised learning uses labeled historical data, while reinforcement learning learns from rewards and penalties received through interaction with an environment.

Supervised learning is used for clustering, while reinforcement learning is used for classification.

Supervised learning requires a reward signal, while reinforcement learning uses labeled examples.

Supervised learning learns from unlabeled data, while reinforcement learning learns from labeled data.

A data scientist trains a decision tree on a small dataset and achieves 100% accuracy on the training data but only 65% accuracy on a held-out test set. Which technique would most directly address this problem?

Prune the decision tree or limit its depth

Remove all categorical features

Increase the number of training epochs

GIAC Machine Learning Engineer

Domain 2Objective 3

Supervised Learning GMLE Practice Questions (Page 4)

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 16–20

16application · easy
A logistics company wants to predict the delivery time (in hours) for packages based on distance, traffic conditions, and weather. The target variable is a continuous value. Which algorithm and evaluation metric are most appropriate?
Select an answer first
17foundation · easy
Which statement best distinguishes supervised learning from reinforcement learning?
Select an answer first
18application · medium
A data scientist trains a decision tree on a small dataset and achieves 100% accuracy on the training data but only 65% accuracy on a held-out test set. Which technique would most directly address this problem?
Select an answer first
19application · medium
A hospital builds a classifier to identify patients with a rare disease. The dataset has only 2% positive cases. The model achieves 98% accuracy, but it predicts 'no disease' for every patient. Which metric would best reveal this model's poor performance?
Select an answer first
20foundation · easy
Which algorithm is commonly used for both regression and classification and makes decisions by recursively splitting the data based on feature values?
Select an answer first

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Supervised Learning GMLE Practice Questions (Page 4)

Questions 16–20

A logistics company wants to predict the delivery time (in hours) for packages based on distance, traffic conditions, and weather. The target variable is a continuous value. Which algorithm and evaluation metric are most appropriate?

Which statement best distinguishes supervised learning from reinforcement learning?

A data scientist trains a decision tree on a small dataset and achieves 100% accuracy on the training data but only 65% accuracy on a held-out test set. Which technique would most directly address this problem?

A hospital builds a classifier to identify patients with a rare disease. The dataset has only 2% positive cases. The model achieves 98% accuracy, but it predicts 'no disease' for every patient. Which metric would best reveal this model's poor performance?

Which algorithm is commonly used for both regression and classification and makes decisions by recursively splitting the data based on feature values?

Keep studying — Machine Learning Algorithms