In K-Means clustering, what does the 'K' represent?

The number of features in the dataset

The number of iterations to run

The number of data points in the dataset

A team is clustering customer reviews to identify common themes. They have a large dataset with thousands of features after TF-IDF vectorization. They want to reduce the dimensionality before clustering to improve performance and interpretability. Which technique is most appropriate?

Principal Component Analysis (PCA)

Feature selection by variance threshold

A credit-card fraud team clusters transactions to find unusual spending patterns. They scale all features to zero mean and unit variance, then run K-Means with k=5. The silhouette score is 0.12, and the elbow plot shows no clear bend. The team suspects the Euclidean distance is not appropriate for their high-dimensional sparse data. Which approach best addresses their concern?

Switch to cosine distance and re-run K-Means with the same k

Apply a dimensionality reduction technique, then re-run K-Means with Euclidean distance

Normalize each row to unit length and use spherical K-Means with cosine similarity

Use a different distance metric, such as Manhattan, and re-run K-Means

What is a common approach to handle categorical variables in clustering?

Replace categorical values with the mean of the cluster

Use them directly as numbers without any transformation

Apply one-hot encoding to convert them to numerical values

Remove all categorical variables from the dataset

GIAC Machine Learning Engineer

Domain 2Objective 1

Clustering GMLE Practice Questions (Page 8)

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 36–40

36foundation · easy
In K-Means clustering, what does the 'K' represent?
Select an answer first
37application · medium
A team is clustering customer reviews to identify common themes. They have a large dataset with thousands of features after TF-IDF vectorization. They want to reduce the dimensionality before clustering to improve performance and interpretability. Which technique is most appropriate?
Select an answer first
38application · medium
A credit-card fraud team clusters transactions to find unusual spending patterns. They scale all features to zero mean and unit variance, then run K-Means with k=5. The silhouette score is 0.12, and the elbow plot shows no clear bend. The team suspects the Euclidean distance is not appropriate for their high-dimensional sparse data. Which approach best addresses their concern?
Select an answer first
39foundation · easy
What is a common approach to handle categorical variables in clustering?
Select an answer first
40foundation · easy
Which algorithm is commonly used to estimate the parameters of a Gaussian Mixture Model?
Select an answer first

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Clustering GMLE Practice Questions (Page 8)

Questions 36–40

In K-Means clustering, what does the 'K' represent?

A team is clustering customer reviews to identify common themes. They have a large dataset with thousands of features after TF-IDF vectorization. They want to reduce the dimensionality before clustering to improve performance and interpretability. Which technique is most appropriate?

What is a common approach to handle categorical variables in clustering?

Which algorithm is commonly used to estimate the parameters of a Gaussian Mixture Model?

Keep studying — Machine Learning Algorithms