QMB3302 UF FALL FINAL VERSION 1,2 &3 (3
LATEST VERSIONS) EXAM 2025/2026 WITH
ACTUAL CORRECT QUESTIONS AND
VERIFIED DETAILED ANSWERS
|FREQUENTLY TESTED QUESTIONS AND
SOLUTIONS |ALREADY GRADED
A+|NEWEST|BRAND NEW
VERSION!!|GUARANTEED PASS
What is the terminal node as discussed in the lecture?
a. The first, or initiation, node in a complex decision tree.
b. The income less the age of our sample.
c. The node that results in a terminal error.
d. The last node (sometimes called a leaf if you google the term). The tree doesn't split after
this.
d
Models. such as the random forest model we ran, often have a number of parameters that the
analyst can choose or set. What is the best source of up to date information about the different
parameters that can be set?
a. A textbook
b. The scikit learn documentation.
c. Google or online forums
d. A telex.
b
Random forests are [inputx] interpretable than decision trees.
a. More
b. Less
c. Just as
b
1|Page
,The correct number of clusters in Hierarchical clustering can be determined precisely using
approaches such as silhouette scores.
a. True
b. False
b
In K-Means clustering, the analyst does not need to determine the number of clusters (K), these
are always derived analytically using the k-means algorithm.
a. True
b. False
b
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.
a. True
b. False
a
According to the documentation, a silhouette scores of 1 is the ______ score and -1 is the
_______ score.
best, worst
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?
a. Regression- to understand the effect of one or more variables on the others.
b. Clustering- to develop groups of customers that have similar patterns.
a, b
What is the purpose of the following code:
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
rfm_std = scale.fit_transform(df)
2|Page
,a. to remove missing values
b. Adjust the number of columns being used.
c. to standardize the data
d. the above code doesn't make any sense.
c
The elbow method provides an exact number of clusters for a k-means algorithm.
a. True
b. False
b
Hierarchical clustering is more powerful than K-means, as it allows the researcher to determine
the exact number of clusters to use in the analysis.
a. True
b. False
b
In K-means- the algorithm has multiple iterations. If we have a simple 2d problem, and a k=2, it
begins by assigning the first centroids to a(n) __________________, and then
___________________ of each point or record to the centroid.
random initial starting point, measuring the distance
An example this week was done in a Jupiter like environment called Google Collab. What was
the language that was demonstrated in the videos?
(One cool thing about this is that it looks just like any other package! Installing this on your own
is tricky)
a. R
b. Java
c. C
d. TensorFlow
d
Neural Networks in computing are exactly the same as the neural networks from biology.
a. True
b. False
b
3|Page
, When viewing a diagram of a neural network there are several layers. What is the input layer?
a. These are the X's, or inputs from your data.
b. These are the Y (the Target variable you are interested in)
c. Something you don't see, here there is some computation to transform the X's into the Y.
a
When viewing a diagram of a neural network there are several layers. What is the output layer?
a. These are the X's, or inputs from your data.
b. These are the Y (the Target variable you are interested in)
c. Something you don't see, here there is some computation to transform the X's into the Y.
b
When viewing a diagram of a neural network there are several layers. What is the hidden layer?
a. These are the X's, or inputs from your data.
b. These are the Y (the Target variable you are interested in)
c. Something you don't see, here there is some computation to transform the X's into the Y.
c
Deep Neural Networks have only 1 hidden layer and multiple input layers.
a. True
b. False
b
Each of the connections between nodes as a connection, each of those connections has a(n)
_______________.
a. Silu
b. Activation function
c. Double headed arrows
d. Fit function
b
In the attempt to fit values from the input layer to the output layer, the hidden layer applies
some weights to the input values.
a. True
b. False
a
4|Page
LATEST VERSIONS) EXAM 2025/2026 WITH
ACTUAL CORRECT QUESTIONS AND
VERIFIED DETAILED ANSWERS
|FREQUENTLY TESTED QUESTIONS AND
SOLUTIONS |ALREADY GRADED
A+|NEWEST|BRAND NEW
VERSION!!|GUARANTEED PASS
What is the terminal node as discussed in the lecture?
a. The first, or initiation, node in a complex decision tree.
b. The income less the age of our sample.
c. The node that results in a terminal error.
d. The last node (sometimes called a leaf if you google the term). The tree doesn't split after
this.
d
Models. such as the random forest model we ran, often have a number of parameters that the
analyst can choose or set. What is the best source of up to date information about the different
parameters that can be set?
a. A textbook
b. The scikit learn documentation.
c. Google or online forums
d. A telex.
b
Random forests are [inputx] interpretable than decision trees.
a. More
b. Less
c. Just as
b
1|Page
,The correct number of clusters in Hierarchical clustering can be determined precisely using
approaches such as silhouette scores.
a. True
b. False
b
In K-Means clustering, the analyst does not need to determine the number of clusters (K), these
are always derived analytically using the k-means algorithm.
a. True
b. False
b
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.
a. True
b. False
a
According to the documentation, a silhouette scores of 1 is the ______ score and -1 is the
_______ score.
best, worst
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?
a. Regression- to understand the effect of one or more variables on the others.
b. Clustering- to develop groups of customers that have similar patterns.
a, b
What is the purpose of the following code:
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
rfm_std = scale.fit_transform(df)
2|Page
,a. to remove missing values
b. Adjust the number of columns being used.
c. to standardize the data
d. the above code doesn't make any sense.
c
The elbow method provides an exact number of clusters for a k-means algorithm.
a. True
b. False
b
Hierarchical clustering is more powerful than K-means, as it allows the researcher to determine
the exact number of clusters to use in the analysis.
a. True
b. False
b
In K-means- the algorithm has multiple iterations. If we have a simple 2d problem, and a k=2, it
begins by assigning the first centroids to a(n) __________________, and then
___________________ of each point or record to the centroid.
random initial starting point, measuring the distance
An example this week was done in a Jupiter like environment called Google Collab. What was
the language that was demonstrated in the videos?
(One cool thing about this is that it looks just like any other package! Installing this on your own
is tricky)
a. R
b. Java
c. C
d. TensorFlow
d
Neural Networks in computing are exactly the same as the neural networks from biology.
a. True
b. False
b
3|Page
, When viewing a diagram of a neural network there are several layers. What is the input layer?
a. These are the X's, or inputs from your data.
b. These are the Y (the Target variable you are interested in)
c. Something you don't see, here there is some computation to transform the X's into the Y.
a
When viewing a diagram of a neural network there are several layers. What is the output layer?
a. These are the X's, or inputs from your data.
b. These are the Y (the Target variable you are interested in)
c. Something you don't see, here there is some computation to transform the X's into the Y.
b
When viewing a diagram of a neural network there are several layers. What is the hidden layer?
a. These are the X's, or inputs from your data.
b. These are the Y (the Target variable you are interested in)
c. Something you don't see, here there is some computation to transform the X's into the Y.
c
Deep Neural Networks have only 1 hidden layer and multiple input layers.
a. True
b. False
b
Each of the connections between nodes as a connection, each of those connections has a(n)
_______________.
a. Silu
b. Activation function
c. Double headed arrows
d. Fit function
b
In the attempt to fit values from the input layer to the output layer, the hidden layer applies
some weights to the input values.
a. True
b. False
a
4|Page
