1 | Page
QMB3302 Final UF Questions and Answers
The correct number of clusters in Hierarchical clustering can be
determined precisely using approaches such as silhouette scores
(True or False)
Ans: False
In K Means clustering, the analyst does not need to determine the
number of clusters (K), these are always derived analytically using
the kmeans algorithm. (True or False)
Ans: False
One big difference between the unsupervised approaches in this
module, and the supervised approaches in prior modules:
Unsupervised models do not have a target variable (Y). This make
is difficult to know when they are "right" or correct. (True or False)
Ans: True
According to the documentation, a silhouette scores of 1 ia
Ans: The best score
According to the documentation, a silhouette score of -1 is
Ans: The worst score
Select all that apply. Imagine you have a data set with
columns/inputs for customers:
Column 1 = Customer ID (a number)
Column 2 = Sales (a dollar value)
Column 3= Frequency (a number)
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Column 4 = Satisfaction (a number)
You would like to understand the impact of Frequency on
customer Satisfaction. What types of approaches could you use?
Note that the type of data is brackets () after the column name.
Ans: Decision tree, random forest, linear regression
Select all that apply. Imagine you have a dataset with the
following columns (inputs) for a set of customers.
Column 1 = Customer ID
Column 2 = Distance to Store
Column 3= Yearly spend
Column 4 = Likelihood to return (a survey response that indicates
a customer is likely to shop again)
What kind of approaches could you use to understand more about
these customers? Why?
Ans: Regression - to udnerstand the effect of one or more variables on
the others
Clustering-to develop groups of customers that have similar patterns
What is the purpose of the following code?
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
rfm_std = scale.fit_transform(df)
Ans: To standardize the data
The elbow method provides an exact number of clusters for a
kmeans algorithm. (True or False)
© 2025 All rights reserved
QMB3302 Final UF Questions and Answers
The correct number of clusters in Hierarchical clustering can be
determined precisely using approaches such as silhouette scores
(True or False)
Ans: False
In K Means clustering, the analyst does not need to determine the
number of clusters (K), these are always derived analytically using
the kmeans algorithm. (True or False)
Ans: False
One big difference between the unsupervised approaches in this
module, and the supervised approaches in prior modules:
Unsupervised models do not have a target variable (Y). This make
is difficult to know when they are "right" or correct. (True or False)
Ans: True
According to the documentation, a silhouette scores of 1 ia
Ans: The best score
According to the documentation, a silhouette score of -1 is
Ans: The worst score
Select all that apply. Imagine you have a data set with
columns/inputs for customers:
Column 1 = Customer ID (a number)
Column 2 = Sales (a dollar value)
Column 3= Frequency (a number)
© 2025 All rights reserved
, 2 | Page
Column 4 = Satisfaction (a number)
You would like to understand the impact of Frequency on
customer Satisfaction. What types of approaches could you use?
Note that the type of data is brackets () after the column name.
Ans: Decision tree, random forest, linear regression
Select all that apply. Imagine you have a dataset with the
following columns (inputs) for a set of customers.
Column 1 = Customer ID
Column 2 = Distance to Store
Column 3= Yearly spend
Column 4 = Likelihood to return (a survey response that indicates
a customer is likely to shop again)
What kind of approaches could you use to understand more about
these customers? Why?
Ans: Regression - to udnerstand the effect of one or more variables on
the others
Clustering-to develop groups of customers that have similar patterns
What is the purpose of the following code?
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
rfm_std = scale.fit_transform(df)
Ans: To standardize the data
The elbow method provides an exact number of clusters for a
kmeans algorithm. (True or False)
© 2025 All rights reserved
