ISYE 6501 - MIDTERM PRACTICE EXAM
ISYE 6501 - MIDTERM PRACTICE EXAM NEWEST
2025/2026 COMPLETE UESTIONS AND CORRECT
DETAILED ANSWERS (VERIFIED ANSWERS) |BRAND NEW
VERSION!!
1.
A retailer wants to forecast daily demand. They observe demand spikes only when
a competitor store offers promotions. Historical competitor promotions are
available. Which action will most likely improve forecast accuracy?
A. Add competitor promotion as an exogenous variable
B. Increase model polynomial degree
C. Apply differencing twice
D. Drop holiday effects
Correct Answer: A
2.
A bank uses a logistic regression model to predict loan default. Model recall is very
low, even though accuracy is high. What is the most likely cause?
A. Overfitting
B. Class imbalance
C. Multicollinearity
D. Too many predictors
Correct Answer: B
3.
A manufacturing plant uses simulation to estimate ueue length. Results are
unstable — small parameter changes produce very different outputs. Why?
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, ISYE 6501 - MIDTERM PRACTICE EXAM
A. Too many replications
B. High variability in the arrival process
C. Unnecessary time-series modeling
D. Not enough machines
Correct Answer: B
4.
A company deploys a k-NN classifier for customer segmentation. As the number of
features increases, performance drops significantly. What explains this?
A. Too large a k
B. Curse of dimensionality
C. Underfitting
D. k too small
Correct Answer: B
5.
A forecast consistently underpredicts sales at the start of each uarter. What
should be added?
A. Trend component
B. uarterly seasonal indicators
C. Random noise terms
D. Regularization
Correct Answer: B
6.
A retail chain notices that adding many correlated advertising metrics reduces
regression model interpretability. What should they do?
A. Increase model depth
B. Use PCA to combine correlated predictors
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, ISYE 6501 - MIDTERM PRACTICE EXAM
C. Add polynomial features
D. Remove intercept
Correct Answer: B
7.
A data scientist builds a linear regression model but finds that residual variance
increases with larger values of the predictor. Which approach is best?
A. Weighted least suares
B. Remove large observations
C. Add more predictors
D. Apply ridge regression
Correct Answer: A
8.
A company wants to minimize transportation cost and must decide which
warehouses supply which stores. What method is appropriate?
A. Logistic regression
B. Linear programming
C. k-NN
D. Classification tree
Correct Answer: B
9.
A customer churn model includes the variable “Churn in next 60 days” as an input.
Model performance appears unrealistically high. Reason?
A. Multicollinearity
B. Underfitting
C. Data leakage
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, ISYE 6501 - MIDTERM PRACTICE EXAM
D. Regularization too low
Correct Answer: C
10.
A model predicts energy consumption well in winter but poorly in summer. What
is the issue?
A. Heterogeneous seasonal patterns
B. Too much training data
C. Over-regularization
D. Underfitting
Correct Answer: A
11.
A simulation model for a call center generates extremely long ueues during peak
times, even though servers are available. Likely cause?
A. Service rate too high
B. Arrival rate exceeds combined service capacity
C. Too many replications
D. Too much randomness
Correct Answer: B
12.
A demand forecast reacts too slowly to sudden changes in the market. What
correction is appropriate?
A. Increase window size
B. Add autoregressive terms
C. Decrease model depth
D. Remove trend
Correct Answer: B
4|Page
ISYE 6501 - MIDTERM PRACTICE EXAM NEWEST
2025/2026 COMPLETE UESTIONS AND CORRECT
DETAILED ANSWERS (VERIFIED ANSWERS) |BRAND NEW
VERSION!!
1.
A retailer wants to forecast daily demand. They observe demand spikes only when
a competitor store offers promotions. Historical competitor promotions are
available. Which action will most likely improve forecast accuracy?
A. Add competitor promotion as an exogenous variable
B. Increase model polynomial degree
C. Apply differencing twice
D. Drop holiday effects
Correct Answer: A
2.
A bank uses a logistic regression model to predict loan default. Model recall is very
low, even though accuracy is high. What is the most likely cause?
A. Overfitting
B. Class imbalance
C. Multicollinearity
D. Too many predictors
Correct Answer: B
3.
A manufacturing plant uses simulation to estimate ueue length. Results are
unstable — small parameter changes produce very different outputs. Why?
1|Page
, ISYE 6501 - MIDTERM PRACTICE EXAM
A. Too many replications
B. High variability in the arrival process
C. Unnecessary time-series modeling
D. Not enough machines
Correct Answer: B
4.
A company deploys a k-NN classifier for customer segmentation. As the number of
features increases, performance drops significantly. What explains this?
A. Too large a k
B. Curse of dimensionality
C. Underfitting
D. k too small
Correct Answer: B
5.
A forecast consistently underpredicts sales at the start of each uarter. What
should be added?
A. Trend component
B. uarterly seasonal indicators
C. Random noise terms
D. Regularization
Correct Answer: B
6.
A retail chain notices that adding many correlated advertising metrics reduces
regression model interpretability. What should they do?
A. Increase model depth
B. Use PCA to combine correlated predictors
2|Page
, ISYE 6501 - MIDTERM PRACTICE EXAM
C. Add polynomial features
D. Remove intercept
Correct Answer: B
7.
A data scientist builds a linear regression model but finds that residual variance
increases with larger values of the predictor. Which approach is best?
A. Weighted least suares
B. Remove large observations
C. Add more predictors
D. Apply ridge regression
Correct Answer: A
8.
A company wants to minimize transportation cost and must decide which
warehouses supply which stores. What method is appropriate?
A. Logistic regression
B. Linear programming
C. k-NN
D. Classification tree
Correct Answer: B
9.
A customer churn model includes the variable “Churn in next 60 days” as an input.
Model performance appears unrealistically high. Reason?
A. Multicollinearity
B. Underfitting
C. Data leakage
3|Page
, ISYE 6501 - MIDTERM PRACTICE EXAM
D. Regularization too low
Correct Answer: C
10.
A model predicts energy consumption well in winter but poorly in summer. What
is the issue?
A. Heterogeneous seasonal patterns
B. Too much training data
C. Over-regularization
D. Underfitting
Correct Answer: A
11.
A simulation model for a call center generates extremely long ueues during peak
times, even though servers are available. Likely cause?
A. Service rate too high
B. Arrival rate exceeds combined service capacity
C. Too many replications
D. Too much randomness
Correct Answer: B
12.
A demand forecast reacts too slowly to sudden changes in the market. What
correction is appropriate?
A. Increase window size
B. Add autoregressive terms
C. Decrease model depth
D. Remove trend
Correct Answer: B
4|Page
