Page 1 of 456
ISYE 6501 FINAL EXAM COMPLETE 300
QUESTIONS WITH DETAILED SOLUTIONS JUST
RELEASED THIS YEAR
ISYE 6501 FINAL EXAM
Introduction to Analytics Modeling
EXAM COVERAGE SUMMARY
This comprehensive final examination covers the full scope of Introduction to Analytics
Modeling as presented in the ISYE 6501 curriculum at Georgia Institute of Technology.
Foundational concepts include the three pillars of analytics (descriptive, predictive,
prescriptive), data types (structured/unstructured, quantitative/categorical, time-series), and
the analytics modeling process. Classification methods are covered extensively, including k-
Nearest Neighbors (k-NN), Support Vector Machines (SVM), Logistic Regression, Classification
and Regression Trees (CART), and Random Forests. Clustering techniques include k-means and
hierarchical clustering. Regression methods include Linear Regression, Ridge Regression (L2),
and LASSO Regression (L1), along with model validation techniques such as cross-validation
and the bias-variance trade-off. Time-series analysis covers ARIMA, Exponential Smoothing,
and GARCH models for volatility. Optimization topics include linear programming, assignment
problems, and approximate dynamic programming. Experimental design principles include
fractional factorial designs. Model evaluation metrics include confusion matrices, sensitivity,
specificity, AUC, AIC, and R-squared. The exam emphasizes analytical problem-solving, model
selection, interpretation of results, and practical applications of analytics in engineering and
business contexts.
INSTRUCTIONS
This examination consists of 300 multiple-choice questions
Select the single best answer for each question
Each question is worth 1 point
1
SUCCESS!
,Page 2 of 456
Total examination time: 3 hours
All questions are randomized and do not follow subtopic domains
Read each question carefully before selecting your answer
Mark your answers on the provided answer sheet
SECTION I: QUESTIONS 1–100
1. What do descriptive questions ask in analytics?
A. What action would be best?
B. What will happen?
C. What happened?
D. Why did it happen?
Answer: C
Descriptive analytics answers "What happened?" by summarizing historical data to identify
patterns and trends, such as determining which customers are most alike.
2
SUCCESS!
,Page 3 of 456
2. What do predictive questions ask in analytics?
A. What happened?
B. What will happen?
C. What action would be best?
D. How did it happen?
Answer: B
Predictive analytics answers "What will happen?" by using historical data to forecast future
outcomes, such as predicting Google's stock price.
3. What do prescriptive questions ask in analytics?
A. What happened?
B. What will happen?
C. What action(s) would be best?
D. Why did it happen?
Answer: C
3
SUCCESS!
, Page 4 of 456
Prescriptive analytics answers "What action(s) would be best?" by recommending optimal
decisions, such as determining where to place traffic lights.
4. What is a model in the context of analytics?
A. A physical representation of a system
B. A real-life situation expressed as mathematics
C. A computer program that makes decisions
D. A collection of raw data
Answer: B
A model is a mathematical representation of a real-life situation that allows us to analyze,
predict, and optimize outcomes.
5. What do classifiers help you do?
A. Predict continuous values
B. Differentiate between categories
4
SUCCESS!
ISYE 6501 FINAL EXAM COMPLETE 300
QUESTIONS WITH DETAILED SOLUTIONS JUST
RELEASED THIS YEAR
ISYE 6501 FINAL EXAM
Introduction to Analytics Modeling
EXAM COVERAGE SUMMARY
This comprehensive final examination covers the full scope of Introduction to Analytics
Modeling as presented in the ISYE 6501 curriculum at Georgia Institute of Technology.
Foundational concepts include the three pillars of analytics (descriptive, predictive,
prescriptive), data types (structured/unstructured, quantitative/categorical, time-series), and
the analytics modeling process. Classification methods are covered extensively, including k-
Nearest Neighbors (k-NN), Support Vector Machines (SVM), Logistic Regression, Classification
and Regression Trees (CART), and Random Forests. Clustering techniques include k-means and
hierarchical clustering. Regression methods include Linear Regression, Ridge Regression (L2),
and LASSO Regression (L1), along with model validation techniques such as cross-validation
and the bias-variance trade-off. Time-series analysis covers ARIMA, Exponential Smoothing,
and GARCH models for volatility. Optimization topics include linear programming, assignment
problems, and approximate dynamic programming. Experimental design principles include
fractional factorial designs. Model evaluation metrics include confusion matrices, sensitivity,
specificity, AUC, AIC, and R-squared. The exam emphasizes analytical problem-solving, model
selection, interpretation of results, and practical applications of analytics in engineering and
business contexts.
INSTRUCTIONS
This examination consists of 300 multiple-choice questions
Select the single best answer for each question
Each question is worth 1 point
1
SUCCESS!
,Page 2 of 456
Total examination time: 3 hours
All questions are randomized and do not follow subtopic domains
Read each question carefully before selecting your answer
Mark your answers on the provided answer sheet
SECTION I: QUESTIONS 1–100
1. What do descriptive questions ask in analytics?
A. What action would be best?
B. What will happen?
C. What happened?
D. Why did it happen?
Answer: C
Descriptive analytics answers "What happened?" by summarizing historical data to identify
patterns and trends, such as determining which customers are most alike.
2
SUCCESS!
,Page 3 of 456
2. What do predictive questions ask in analytics?
A. What happened?
B. What will happen?
C. What action would be best?
D. How did it happen?
Answer: B
Predictive analytics answers "What will happen?" by using historical data to forecast future
outcomes, such as predicting Google's stock price.
3. What do prescriptive questions ask in analytics?
A. What happened?
B. What will happen?
C. What action(s) would be best?
D. Why did it happen?
Answer: C
3
SUCCESS!
, Page 4 of 456
Prescriptive analytics answers "What action(s) would be best?" by recommending optimal
decisions, such as determining where to place traffic lights.
4. What is a model in the context of analytics?
A. A physical representation of a system
B. A real-life situation expressed as mathematics
C. A computer program that makes decisions
D. A collection of raw data
Answer: B
A model is a mathematical representation of a real-life situation that allows us to analyze,
predict, and optimize outcomes.
5. What do classifiers help you do?
A. Predict continuous values
B. Differentiate between categories
4
SUCCESS!
