Which metric is an external clustering evaluation metric that measures the similarity between two clusterings?

Within-cluster sum of squares (WCSS)

An e-commerce company uses K-Means to segment products based on price, rating, and number of reviews. They run K-Means with k from 2 to 10 and compute the silhouette score for each k. The silhouette score is highest at k=4, but the elbow method suggests k=6. The business wants a segmentation that is both interpretable and stable. What should they do?

Run K-Means multiple times with different initializations for k=4 and k=6 and compare stability

Choose k=4 because the silhouette score is highest

Use the average of the two k values, k=5

Choose k=6 because the elbow method is more reliable

A marketing team wants to segment customers based on purchasing behavior. They have data that includes both high-spending and low-spending customers, but some customers have overlapping behaviors. They need a model that provides a probability of membership for each customer to each segment. Which approach should they use?

Gaussian Mixture Model with 3 components

Hierarchical clustering with Ward's linkage

Why is feature scaling important before applying K-Means clustering?

It ensures that all features contribute equally to the distance calculation

It makes the algorithm run faster

It converts categorical variables to numerical ones

It reduces the number of features in the dataset

GIAC Machine Learning Engineer

Domain 2Objective 1

Clustering 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 45 practice questions to prepare you well beyond it. (estimate)

45questions here

9free pages

8concepts

Questions 11–15

11foundation · easy
Which metric is an external clustering evaluation metric that measures the similarity between two clusterings?
Select an answer first
12application · medium
An e-commerce company uses K-Means to segment products based on price, rating, and number of reviews. They run K-Means with k from 2 to 10 and compute the silhouette score for each k. The silhouette score is highest at k=4, but the elbow method suggests k=6. The business wants a segmentation that is both interpretable and stable. What should they do?
Select an answer first
13application · medium
A marketing team wants to segment customers based on purchasing behavior. They have data that includes both high-spending and low-spending customers, but some customers have overlapping behaviors. They need a model that provides a probability of membership for each customer to each segment. Which approach should they use?
Select an answer first
14foundation · easy
Why is feature scaling important before applying K-Means clustering?
Select an answer first
15foundation · easy
Given two points A = (1, 2) and B = (4, 6), what is the Euclidean distance between them?
Select an answer first

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Clustering GMLE Practice Questions (Page 3)

Questions 11–15

Which metric is an external clustering evaluation metric that measures the similarity between two clusterings?

Why is feature scaling important before applying K-Means clustering?

Given two points A = (1, 2) and B = (4, 6), what is the Euclidean distance between them?

Keep studying — Machine Learning Algorithms