A company wants to automatically categorize customer support tickets into 'billing', 'technical', or 'account access' issues. They have a historical dataset of tickets with manually assigned categories. Which machine learning approach should they use?

Unsupervised clustering with k-means

Supervised classification with a support vector machine

Reinforcement learning with a reward function

Dimensionality reduction with PCA

A medical diagnostics company develops a classifier to detect a rare disease. The cost of a false negative is extremely high (patient misses treatment), while false positives only lead to additional testing. The model currently has precision of 0.9 and recall of 0.5. Which change is most likely to improve recall without drastically reducing precision?

Use accuracy as the primary metric

Raise the decision threshold for classifying a case as positive

Lower the decision threshold for classifying a case as positive

A team is building a model to predict housing prices. They have 1,000 samples and 500 features. The model achieves low training error but high test error. They suspect overfitting due to the high dimensionality. Which approach is most effective to reduce overfitting while retaining predictive power?

Add more features to capture more information

Remove the test set and use all data for training

Use a more complex model like a deep neural network

Apply L2 regularization (Ridge) to shrink coefficients and use cross-validation to tune the penalty

GIAC Machine Learning Engineer

Domain 2Objective 3

Supervised Learning GMLE Practice Questions (Page 3)

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 11–15

11application · medium
A company wants to automatically categorize customer support tickets into 'billing', 'technical', or 'account access' issues. They have a historical dataset of tickets with manually assigned categories. Which machine learning approach should they use?
Select an answer first
12expert · hard
A medical diagnostics company develops a classifier to detect a rare disease. The cost of a false negative is extremely high (patient misses treatment), while false positives only lead to additional testing. The model currently has precision of 0.9 and recall of 0.5. Which change is most likely to improve recall without drastically reducing precision?
Select an answer first
13expert · hard
A team is building a model to predict housing prices. They have 1,000 samples and 500 features. The model achieves low training error but high test error. They suspect overfitting due to the high dimensionality. Which approach is most effective to reduce overfitting while retaining predictive power?
Select an answer first
14application · medium
A model is built to classify emails as 'spam' or 'not spam'. The test set contains 100 spam emails and 900 non-spam emails. The model predicts 80 of the spam emails correctly and 850 of the non-spam emails correctly. What is the model's precision for the spam class?
Select an answer first
15application · medium
A model predicts whether an email is spam. In a test set of 100 emails, 10 are actually spam. The model correctly identifies 8 spam emails and incorrectly flags 5 legitimate emails as spam. What is the precision of the model?
Select an answer first

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Supervised Learning GMLE Practice Questions (Page 3)

Questions 11–15

A company wants to automatically categorize customer support tickets into 'billing', 'technical', or 'account access' issues. They have a historical dataset of tickets with manually assigned categories. Which machine learning approach should they use?

A model is built to classify emails as 'spam' or 'not spam'. The test set contains 100 spam emails and 900 non-spam emails. The model predicts 80 of the spam emails correctly and 850 of the non-spam emails correctly. What is the model's precision for the spam class?

A model predicts whether an email is spam. In a test set of 100 emails, 10 are actually spam. The model correctly identifies 8 spam emails and incorrectly flags 5 legitimate emails as spam. What is the precision of the model?

Keep studying — Machine Learning Algorithms