1|Page
QMB3302 UF FALL FINAL EXAM VERSION 1,2&3 NEWEST 2025/2026
ACTUAL EXAM WITH COMPLETE QUESTIONS AND CORRECT
DETAILED ANSWERS (100% VERIFIED ANSWERS) |ALREADY
GRADED A+| ||PROFESSOR VERIFIED||
Which of the following statements best describes an ensemble
method in machine learning?
a. A technique that combines the results of multiple models to
improve overall predictive accuracy,
b. An algorithm that learns to find patterns and relationships in
data without being explicitly programmed,
c. A method that automatically groups similar data points into
clusters based on their characteristics.
d. A model that predicts the value of a dependent variable based
on the values of one or more independent variables. - ANSWER-a
Which of the following best describes supervised learning?
a. A machine learning approach where the algorithm learns to
optimize a performance metric by adjusting its internal
parameters.
b. A machine learning approach where the algorithm automatically
groups similar data points into clusters.
,2|Page
c. A machines learning approach where the algorithm receives
labeled data and learns to map inputs to outputs based on those
labels.
d. A machine learning approach where the algorithm learns to find
patterns and relationships in data without being explicitly
programmed. - ANSWER-c
Which of the following statements best describes classification in
machine learning?
a. A type of supervised learning where the goal is to predict a
continuous target based on input features.
b. A type of reinforcement learning where the goal is to learn an
optimal policy for making decisions in an environment.
c. A type of unsupervised learning where the goal is to group
similar data points into clusters.
d. A type of supervised learning where the goal is to assign input
data points to predefined categories or classes. - ANSWER-d
We want the R-squared value for our regression model to be
100%.
,3|Page
a. True
b. Fales - ANSWER-b
One weakness of cross-validation discussed is that information
can sometimes _______ across different periods. A common
situation in which this happens is when we are looking at stock
data.
a. Leak
b. Overfit
c. Not leak
d. Underfit - ANSWER-a
In which of these situations would you want to use a clustering
algorithm?
a. You were given the financial data for the Federal Reserve of
New York in 2023 and want to determine where the discrepancy
in accumulated depreciation came from before you submit the
financial statements.
, 4|Page
b. You have a dataset containing past crimes of current
defendants and you want to determine the likelihood that they will
commit another crime.
c. You have a dataset containing 2023 Charlotte, NC housing data
and you want to predict 2024 housing prices.
d. You have a dataset containing set for Cheesecake Factory and
you want to look at customer spending at the restaurant in order
to find patterns among customers who share similar
characteristics. - ANSWER-d
What is a potential downside of using linear regression models in
machine learning?
a. They are not suitable for predicting continuous target variables.
b. They are too complex and difficult to interpret.
c. They are prone to overfitting the data.
d. They can only handle numerical data. - ANSWER-c
What type of algorithm would you use to segment customers into
groups?
Assume the groups ARE already labeled.
QMB3302 UF FALL FINAL EXAM VERSION 1,2&3 NEWEST 2025/2026
ACTUAL EXAM WITH COMPLETE QUESTIONS AND CORRECT
DETAILED ANSWERS (100% VERIFIED ANSWERS) |ALREADY
GRADED A+| ||PROFESSOR VERIFIED||
Which of the following statements best describes an ensemble
method in machine learning?
a. A technique that combines the results of multiple models to
improve overall predictive accuracy,
b. An algorithm that learns to find patterns and relationships in
data without being explicitly programmed,
c. A method that automatically groups similar data points into
clusters based on their characteristics.
d. A model that predicts the value of a dependent variable based
on the values of one or more independent variables. - ANSWER-a
Which of the following best describes supervised learning?
a. A machine learning approach where the algorithm learns to
optimize a performance metric by adjusting its internal
parameters.
b. A machine learning approach where the algorithm automatically
groups similar data points into clusters.
,2|Page
c. A machines learning approach where the algorithm receives
labeled data and learns to map inputs to outputs based on those
labels.
d. A machine learning approach where the algorithm learns to find
patterns and relationships in data without being explicitly
programmed. - ANSWER-c
Which of the following statements best describes classification in
machine learning?
a. A type of supervised learning where the goal is to predict a
continuous target based on input features.
b. A type of reinforcement learning where the goal is to learn an
optimal policy for making decisions in an environment.
c. A type of unsupervised learning where the goal is to group
similar data points into clusters.
d. A type of supervised learning where the goal is to assign input
data points to predefined categories or classes. - ANSWER-d
We want the R-squared value for our regression model to be
100%.
,3|Page
a. True
b. Fales - ANSWER-b
One weakness of cross-validation discussed is that information
can sometimes _______ across different periods. A common
situation in which this happens is when we are looking at stock
data.
a. Leak
b. Overfit
c. Not leak
d. Underfit - ANSWER-a
In which of these situations would you want to use a clustering
algorithm?
a. You were given the financial data for the Federal Reserve of
New York in 2023 and want to determine where the discrepancy
in accumulated depreciation came from before you submit the
financial statements.
, 4|Page
b. You have a dataset containing past crimes of current
defendants and you want to determine the likelihood that they will
commit another crime.
c. You have a dataset containing 2023 Charlotte, NC housing data
and you want to predict 2024 housing prices.
d. You have a dataset containing set for Cheesecake Factory and
you want to look at customer spending at the restaurant in order
to find patterns among customers who share similar
characteristics. - ANSWER-d
What is a potential downside of using linear regression models in
machine learning?
a. They are not suitable for predicting continuous target variables.
b. They are too complex and difficult to interpret.
c. They are prone to overfitting the data.
d. They can only handle numerical data. - ANSWER-c
What type of algorithm would you use to segment customers into
groups?
Assume the groups ARE already labeled.
