FREE Data Science Unsupervised Learning Techniques Questions and Answers

Question 1

A data scientist is tasked with reducing the dimensionality of a large dataset containing highly correlated features.
The goal is to create a smaller set of new, uncorrelated features that capture the maximum possible variance from the original data.
Which unsupervised learning technique is most suitable for this purpose?

Accepted Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Answer

Apriori Algorithm

Answer

DBSCAN

Question 2

A retail company wants to analyze customer transaction data to discover which products are frequently purchased together. This information will be used for product placement and promotional strategies. Which of the following algorithms is designed for this type of market basket analysis?

Accepted Answer

Apriori Algorithm

Answer

Hierarchical Clustering

Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Question 3

A data science team is working with a dataset that has complex, non-spherical clusters and contains a significant amount of noise. The team does not know the number of clusters in advance. Which clustering algorithm would be the most effective choice for this scenario?

Accepted Answer

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Answer

K-Means Clustering

Answer

Agglomerative Hierarchical Clustering

Answer

Ward's Method

Question 4

Which of the following is a primary disadvantage of using hierarchical clustering algorithms, especially the agglomerative type, compared to K-Means clustering?

Accepted Answer

It is computationally expensive and has high memory requirements for large datasets.

Answer

It requires the number of clusters to be specified in advance.

Answer

It is highly sensitive to the initial placement of centroids.

Answer

It struggles with non-spherical or arbitrarily shaped clusters.

Question 5

A data analyst is using an agglomerative hierarchical clustering algorithm and needs to decide how to measure the distance between clusters. They choose a method where the distance between two clusters is defined as the distance between the two closest points in the different clusters. What is this linkage criterion called?

Accepted Answer

Single Linkage

Answer

Complete Linkage

Answer

Average Linkage

Answer

Ward's Linkage

Question 6

In Principal Component Analysis (PCA), what does the first principal component represent?

Accepted Answer

The direction in the feature space along which the data has the maximum variance.

Answer

The direction in the feature space with the least variance.

Answer

A randomly selected feature from the original dataset.

Answer

The linear combination of original features that is most correlated with the target variable.