A data scientist is clustering geographical locations of stores using latitude and longitude. The clusters are expected to be irregularly shaped, and some stores are isolated outliers. They plan to use DBSCAN. What is the most important consideration when setting the epsilon parameter?

Epsilon should be set to the maximum distance between any two points to ensure all points are clustered.

Epsilon should be set to the average distance between all pairs of points.

Epsilon should be chosen based on the scale of the data, often using a k-distance plot, because it defines the radius for neighborhood search.

Epsilon is irrelevant when using latitude and longitude because DBSCAN uses haversine distance automatically.

A researcher is using hierarchical clustering to analyze a dataset of animal species based on multiple morphological features. They want to cut the dendrogram to obtain a specific number of clusters. What does the height of the cut in a dendrogram represent?

The number of data points in each cluster.

The probability that two clusters are related.

The distance or dissimilarity at which clusters are merged.

The average value of the features in each cluster.

A data scientist is comparing K-Means and Gaussian Mixture Models for clustering a dataset. They observe that K-Means produces hard assignments, while GMM produces soft assignments. They want to use the clustering results to estimate the probability that a new customer belongs to each segment. Which approach is more appropriate?

Use GMM but only if the clusters are spherical; otherwise, use K-Means.

Use GMM, because it provides posterior probabilities of cluster membership for new data points.

Use K-Means and then train a separate classifier to predict probabilities.

Use K-Means, because it is faster and can be used to assign probabilities by distance to centroids.

GIAC Machine Learning Engineer

Domain 2Objective 1

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

45questions here

9free pages

8concepts

Questions 16–20

16foundation · easy
Which of the following is an internal clustering evaluation metric?
Select an answer first
17foundation · easy
Which distance metric is most appropriate for comparing text documents represented as word frequency vectors?
Select an answer first
18application · medium
A data scientist is clustering geographical locations of stores using latitude and longitude. The clusters are expected to be irregularly shaped, and some stores are isolated outliers. They plan to use DBSCAN. What is the most important consideration when setting the epsilon parameter?
Select an answer first
19application · medium
A researcher is using hierarchical clustering to analyze a dataset of animal species based on multiple morphological features. They want to cut the dendrogram to obtain a specific number of clusters. What does the height of the cut in a dendrogram represent?
Select an answer first
20expert · hard
A data scientist is comparing K-Means and Gaussian Mixture Models for clustering a dataset. They observe that K-Means produces hard assignments, while GMM produces soft assignments. They want to use the clustering results to estimate the probability that a new customer belongs to each segment. Which approach is more appropriate?
Select an answer first

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Clustering GMLE Practice Questions (Page 4)

Questions 16–20

Which of the following is an internal clustering evaluation metric?

Which distance metric is most appropriate for comparing text documents represented as word frequency vectors?

A data scientist is clustering geographical locations of stores using latitude and longitude. The clusters are expected to be irregularly shaped, and some stores are isolated outliers. They plan to use DBSCAN. What is the most important consideration when setting the epsilon parameter?

A researcher is using hierarchical clustering to analyze a dataset of animal species based on multiple morphological features. They want to cut the dendrogram to obtain a specific number of clusters. What does the height of the cut in a dendrogram represent?

Keep studying — Machine Learning Algorithms