MBA BUSINESS ANALYTICS
FINAL EXAM 2026-2027||questions
and answers with rationales/graded
A+/2026 update/100% correct
/instant download
Full Exam Prep | 80+ Questions with Rationales
Time: 3 Hours
Total Marks: 100
Instructions: Select the best option for each question. Correct answers
are highlighted in bold.
SECTION A: FOUNDATIONS OF BUSINESS ANALYTICS (Q1–15)
1. Which of the following best defines Business Analytics?
a) Collecting and storing data in a data warehouse
b) Using statistical and quantitative analysis to derive insights from data to drive
business decisions
c) Writing SQL queries to extract data
d) Creating dashboards for reporting only
Rationale: Business analytics focuses on using data, statistics, and quantitative
methods to inform strategic and operational decisions, not just reporting or storage.
2. The three main types of analytics are:
a) Structured, semi-structured, unstructured
b) Descriptive, predictive, prescriptive
c) Qualitative, quantitative, mixed
d) Internal, external, real-time
Rationale: Descriptive (what happened), predictive (what could happen), and
prescriptive (what should be done) are the core pillars of business analytics.
,3. A retail company analyzes past sales data to identify seasonal trends. This is
an example of:
a) Predictive analytics
b) Prescriptive analytics
c) Descriptive analytics
d) Cognitive analytics
Rationale: Descriptive analytics summarizes historical data to understand patterns
and trends.
4. Which analytics type uses optimization and simulation to recommend
actions?
a) Descriptive
b) Predictive
c) Prescriptive
d) Diagnostic
Rationale: Prescriptive analytics suggests decision options based on predictions
and constraints.
5. In the CRISP-DM framework, which phase consumes the most time in
practice?
a) Business understanding
b) Data preparation
c) Modeling
d) Deployment
Rationale: Data cleaning, transformation, and integration typically take 60–80%
of project time.
6. A confusion matrix is used to evaluate:
a) Regression models
b) Classification models
c) Clustering models
d) Association rules
Rationale: Confusion matrices measure classification performance (TP, TN, FP,
FN).
7. Overfitting in a model means:
a) Model performs well on training data but poorly on unseen data
, b) Model underperforms on training data
c) Model has too few features
d) Model has high bias
Rationale: Overfitting captures noise in training data, failing to generalize.
8. Which metric is most appropriate for imbalanced classification?
a) Accuracy
b) F1-score
c) Mean absolute error
d) R-squared
Rationale: Accuracy can be misleading; F1-score balances precision and recall for
minority classes.
9. A data scientist splits data into 70% train and 30% test. The purpose of the
test set is:
a) To train the model
b) To tune hyperparameters
c) To evaluate final model performance on unseen data
d) To perform feature engineering
Rationale: The test set provides an unbiased estimate of model generalization.
10. Which is a key difference between supervised and unsupervised learning?
a) Supervised uses labeled data; unsupervised does not
b) Unsupervised requires a target variable
c) Supervised is only for regression
d) Unsupervised is more accurate
Rationale: Supervised learning uses input-output pairs; unsupervised finds hidden
patterns without labels.
11. Bias-variance tradeoff: High bias typically leads to:
a) Overfitting
b) Underfitting
c) Low training error
d) High variance
Rationale: High bias means the model is too simple to capture patterns
(underfitting).
FINAL EXAM 2026-2027||questions
and answers with rationales/graded
A+/2026 update/100% correct
/instant download
Full Exam Prep | 80+ Questions with Rationales
Time: 3 Hours
Total Marks: 100
Instructions: Select the best option for each question. Correct answers
are highlighted in bold.
SECTION A: FOUNDATIONS OF BUSINESS ANALYTICS (Q1–15)
1. Which of the following best defines Business Analytics?
a) Collecting and storing data in a data warehouse
b) Using statistical and quantitative analysis to derive insights from data to drive
business decisions
c) Writing SQL queries to extract data
d) Creating dashboards for reporting only
Rationale: Business analytics focuses on using data, statistics, and quantitative
methods to inform strategic and operational decisions, not just reporting or storage.
2. The three main types of analytics are:
a) Structured, semi-structured, unstructured
b) Descriptive, predictive, prescriptive
c) Qualitative, quantitative, mixed
d) Internal, external, real-time
Rationale: Descriptive (what happened), predictive (what could happen), and
prescriptive (what should be done) are the core pillars of business analytics.
,3. A retail company analyzes past sales data to identify seasonal trends. This is
an example of:
a) Predictive analytics
b) Prescriptive analytics
c) Descriptive analytics
d) Cognitive analytics
Rationale: Descriptive analytics summarizes historical data to understand patterns
and trends.
4. Which analytics type uses optimization and simulation to recommend
actions?
a) Descriptive
b) Predictive
c) Prescriptive
d) Diagnostic
Rationale: Prescriptive analytics suggests decision options based on predictions
and constraints.
5. In the CRISP-DM framework, which phase consumes the most time in
practice?
a) Business understanding
b) Data preparation
c) Modeling
d) Deployment
Rationale: Data cleaning, transformation, and integration typically take 60–80%
of project time.
6. A confusion matrix is used to evaluate:
a) Regression models
b) Classification models
c) Clustering models
d) Association rules
Rationale: Confusion matrices measure classification performance (TP, TN, FP,
FN).
7. Overfitting in a model means:
a) Model performs well on training data but poorly on unseen data
, b) Model underperforms on training data
c) Model has too few features
d) Model has high bias
Rationale: Overfitting captures noise in training data, failing to generalize.
8. Which metric is most appropriate for imbalanced classification?
a) Accuracy
b) F1-score
c) Mean absolute error
d) R-squared
Rationale: Accuracy can be misleading; F1-score balances precision and recall for
minority classes.
9. A data scientist splits data into 70% train and 30% test. The purpose of the
test set is:
a) To train the model
b) To tune hyperparameters
c) To evaluate final model performance on unseen data
d) To perform feature engineering
Rationale: The test set provides an unbiased estimate of model generalization.
10. Which is a key difference between supervised and unsupervised learning?
a) Supervised uses labeled data; unsupervised does not
b) Unsupervised requires a target variable
c) Supervised is only for regression
d) Unsupervised is more accurate
Rationale: Supervised learning uses input-output pairs; unsupervised finds hidden
patterns without labels.
11. Bias-variance tradeoff: High bias typically leads to:
a) Overfitting
b) Underfitting
c) Low training error
d) High variance
Rationale: High bias means the model is too simple to capture patterns
(underfitting).
