Data Science Unsupervised Learning Techniques Questions and Answers

Question 1

A data science team is using the K-Means algorithm for customer segmentation.
They notice that running the algorithm multiple times on the same dataset produces slightly different final clusters.
What is the most likely cause of this inconsistent output?

Accepted Answer

K-Means is highly sensitive to the initial random placement of cluster centroids.

Answer

The algorithm automatically adjusts the number of clusters (K) on each run.

Answer

The dataset contains non-numeric features that are handled differently each time.

Answer

The algorithm is robust to outliers, which causes minor shifts in cluster assignments.

Question 2

Which of the following best describes the primary objective of Principal Component Analysis (PCA)?

Accepted Answer

To transform a set of potentially correlated variables into a smaller set of uncorrelated variables while preserving the maximum amount of variance.

Answer

To group similar data points into a predefined number of distinct groups.

Answer

To identify association rules between variables in a transactional dataset.

Answer

To build a hierarchical tree of nested clusters to explore relationships at different levels of granularity.

Question 3

A data scientist needs to analyze customer purchase behavior but does not know the optimal number of customer segments beforehand. They want to visualize the relationships and hierarchy between different potential segments. Which unsupervised learning technique is most suitable for this exploratory task?

Accepted Answer

Agglomerative Hierarchical Clustering

Answer

K-Means Clustering

Answer

Principal Component Analysis (PCA)

Answer

DBSCAN

Question 4

In a retail dataset, the association rule {Diapers} -> {Beer} has a confidence of 0.8. What is the correct interpretation of this value?

Accepted Answer

Of all the transactions that contain Diapers, 80% also contain Beer.

Answer

80% of all transactions in the dataset contain both Diapers and Beer.

Answer

Customers who purchase Diapers are 80% more likely to buy Beer than the average customer.

Answer

Of all the transactions that contain Beer, 80% also contain Diapers.

Question 5

A data analyst is tasked with segmenting a customer dataset that contains clusters of non-spherical shapes and a significant number of outliers. Which clustering algorithm would be the most effective choice in this situation?

Accepted Answer

DBSCAN

Answer

K-Means

Answer

Principal Component Analysis

Answer

Apriori Algorithm

Question 6

A machine learning engineer is preparing a dataset with over 200 features, many of which are highly correlated. To improve model training efficiency and mitigate multicollinearity before applying a supervised learning algorithm, which unsupervised technique is the most appropriate first step?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Hierarchical Clustering

Answer

Apriori Algorithm

Answer

DBSCAN

A data science team is using the K-Means algorithm for customer segmentation.They notice that running the algorithm multiple times on the same dataset produces slightly different final clusters.What is the most likely cause of this inconsistent output?

A data science team is using the K-Means algorithm for customer segmentation.
They notice that running the algorithm multiple times on the same dataset produces slightly different final clusters.
What is the most likely cause of this inconsistent output?