WGU C207 Data-Driven Decision-Making Final
Exam 2026/2027 Latest Update | 180+ Questions
with Verified Answers and Rationales
Q1. A retail company analyzes last quarter’s sales data to identify which
products had the highest revenue. This is an example of:
A. Predictive analytics
B. Prescriptive analytics
C. Descriptive analytics
D. Diagnostic analytics
Rationale:-Descriptive analytics answers “what happened?” by summarizing
historical data. Predictive analytics forecasts future outcomes. Prescriptive
analytics recommends actions. Diagnostic analytics explains why something
happened.
Q2. A logistics company uses optimization algorithms to determine the most
efficient delivery routes based on traffic patterns, fuel costs, and delivery time
windows. This represents:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Prescriptive analytics answers “what should we do?” by using
optimization and simulation to recommend specific actions. Route optimization
with constraints is a classic prescriptive application.
Q3. A hospital dashboard displays real-time patient admission rates, average
length of stay, and readmission percentages. These metrics are best classified
as:
A. Raw data elements
B. Key Performance Indicators (KPIs)
C. Data lake repositories
D. ETL processes
Rationale:-KPIs are measurable values that demonstrate effectiveness in
,achieving key business objectives. Admission rates, length of stay, and readmission
rates are healthcare-specific KPIs.
Q4. In OLAP multidimensional analysis, rotating the axes of a data cube to
view data from a different perspective is called:
A. Slicing
B. Dicing
C. Pivoting
D. Roll-up
Rationale:-Pivoting rotates the axes of a multidimensional cube to view data from
different perspectives (e.g., swapping rows and columns). Slicing selects a subset
of one dimension. Dicing selects a subcube across multiple dimensions. Roll-up
aggregates data to a higher level.
Q5. A data mart differs from a data warehouse in that it:
A. Contains raw, unprocessed data from all organizational sources
B. Is a subset focused on a specific department or business function
C. Requires no ETL process before loading data
D. Only stores unstructured data like images and videos
Rationale:-A data mart is a subject-oriented subset of a data warehouse designed
for a specific department (e.g., sales, marketing, finance). Data warehouses are
enterprise-wide. Both require ETL. Data marts store structured data.
Q6. A company stores social media posts, customer emails, and sensor data in
their original formats without predefined schemas. This storage repository is
best described as:
A. Data warehouse
B. Data mart
C. Data lake
D. Relational database
Rationale:-A data lake stores raw data in native formats (structured, semi-
structured, unstructured) without predefined schemas, enabling flexible future
analysis. Data warehouses and marts use structured, schema-on-write approaches.
Q7. The ETL process in business intelligence stands for:
A. Evaluate, Test, Launch
B. Extract, Transform, Load
C. Enter, Tabulate, List
D. Estimate, Track, Log
,Rationale:-ETL (Extract, Transform, Load) is the standard data integration
process: extract data from source systems, transform it into consistent formats, and
load it into the data warehouse.
Q8. A manufacturing company investigates why production defects increased
in March by analyzing machine maintenance logs, operator training records,
and raw material quality reports. This is:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Diagnostic analytics answers “why did it happen?” by drilling down
into data to identify root causes. Analyzing multiple data sources to explain defect
increases is diagnostic.
Q9. A bank uses a model to predict which customers are likely to default on a
loan within the next six months based on their credit history and transaction
patterns. This is an example of:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Predictive analytics answers “what is likely to happen?” by using
historical data to forecast future outcomes. Default risk prediction is a classic
predictive application.
Q10. Which type of analytics would recommend adjusting inventory levels
based on sales forecasts and supplier lead times?
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Prescriptive analytics recommends specific actions (adjusting
inventory levels) based on forecasts and constraints, directly supporting decision-
making.
Q11. A data warehouse is characterized by all of the following EXCEPT:
A. Subject-oriented
B. Integrated
, C. Volatile (data is non-volatile once stored)
D. Time-variant
Rationale:-Data warehouses are non-volatile (data is not deleted or overwritten).
Subject-oriented, integrated, and time-variant are key characteristics defined by
Inmon.
Q12. Which of the following is an example of a leading KPI?
A. Customer satisfaction score (lagging)
B. Number of sales calls made per day (leading)
C. Quarterly revenue (lagging)
D. Employee turnover rate (lagging)
Rationale:-Leading KPIs predict future performance (e.g., sales calls, website
visits). Lagging KPIs reflect past performance (revenue, turnover, satisfaction).
Q13. In the CRISP-DM framework, the phase where data is cleaned,
transformed, and prepared for modeling is called:
A. Business understanding
B. Data understanding
C. Data preparation
D. Modeling
Rationale:-CRISP-DM (Cross-Industry Standard Process for Data Mining)
phases: Business Understanding, Data Understanding, Data Preparation,
Modeling, Evaluation, Deployment.
Q14. A company uses a dashboard that refreshes every 15 minutes showing
current production line efficiency. This is an example of:
A. Batch processing
B. Real-time analytics (near real-time)
C. Predictive modeling
D. Prescriptive optimization
Rationale:-Real-time or near-real-time analytics processes data as it arrives to
provide up-to-the-minute insights. Batch processing would be daily or hourly
updates.
Q15. Which of the following best describes the difference between supervised
and unsupervised machine learning?
A. Supervised uses labeled data; unsupervised uses unlabeled data
B. Supervised uses unlabeled data; unsupervised uses labeled data
C. Both require labeled data
Exam 2026/2027 Latest Update | 180+ Questions
with Verified Answers and Rationales
Q1. A retail company analyzes last quarter’s sales data to identify which
products had the highest revenue. This is an example of:
A. Predictive analytics
B. Prescriptive analytics
C. Descriptive analytics
D. Diagnostic analytics
Rationale:-Descriptive analytics answers “what happened?” by summarizing
historical data. Predictive analytics forecasts future outcomes. Prescriptive
analytics recommends actions. Diagnostic analytics explains why something
happened.
Q2. A logistics company uses optimization algorithms to determine the most
efficient delivery routes based on traffic patterns, fuel costs, and delivery time
windows. This represents:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Prescriptive analytics answers “what should we do?” by using
optimization and simulation to recommend specific actions. Route optimization
with constraints is a classic prescriptive application.
Q3. A hospital dashboard displays real-time patient admission rates, average
length of stay, and readmission percentages. These metrics are best classified
as:
A. Raw data elements
B. Key Performance Indicators (KPIs)
C. Data lake repositories
D. ETL processes
Rationale:-KPIs are measurable values that demonstrate effectiveness in
,achieving key business objectives. Admission rates, length of stay, and readmission
rates are healthcare-specific KPIs.
Q4. In OLAP multidimensional analysis, rotating the axes of a data cube to
view data from a different perspective is called:
A. Slicing
B. Dicing
C. Pivoting
D. Roll-up
Rationale:-Pivoting rotates the axes of a multidimensional cube to view data from
different perspectives (e.g., swapping rows and columns). Slicing selects a subset
of one dimension. Dicing selects a subcube across multiple dimensions. Roll-up
aggregates data to a higher level.
Q5. A data mart differs from a data warehouse in that it:
A. Contains raw, unprocessed data from all organizational sources
B. Is a subset focused on a specific department or business function
C. Requires no ETL process before loading data
D. Only stores unstructured data like images and videos
Rationale:-A data mart is a subject-oriented subset of a data warehouse designed
for a specific department (e.g., sales, marketing, finance). Data warehouses are
enterprise-wide. Both require ETL. Data marts store structured data.
Q6. A company stores social media posts, customer emails, and sensor data in
their original formats without predefined schemas. This storage repository is
best described as:
A. Data warehouse
B. Data mart
C. Data lake
D. Relational database
Rationale:-A data lake stores raw data in native formats (structured, semi-
structured, unstructured) without predefined schemas, enabling flexible future
analysis. Data warehouses and marts use structured, schema-on-write approaches.
Q7. The ETL process in business intelligence stands for:
A. Evaluate, Test, Launch
B. Extract, Transform, Load
C. Enter, Tabulate, List
D. Estimate, Track, Log
,Rationale:-ETL (Extract, Transform, Load) is the standard data integration
process: extract data from source systems, transform it into consistent formats, and
load it into the data warehouse.
Q8. A manufacturing company investigates why production defects increased
in March by analyzing machine maintenance logs, operator training records,
and raw material quality reports. This is:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Diagnostic analytics answers “why did it happen?” by drilling down
into data to identify root causes. Analyzing multiple data sources to explain defect
increases is diagnostic.
Q9. A bank uses a model to predict which customers are likely to default on a
loan within the next six months based on their credit history and transaction
patterns. This is an example of:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Predictive analytics answers “what is likely to happen?” by using
historical data to forecast future outcomes. Default risk prediction is a classic
predictive application.
Q10. Which type of analytics would recommend adjusting inventory levels
based on sales forecasts and supplier lead times?
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics
D. Prescriptive analytics
Rationale:-Prescriptive analytics recommends specific actions (adjusting
inventory levels) based on forecasts and constraints, directly supporting decision-
making.
Q11. A data warehouse is characterized by all of the following EXCEPT:
A. Subject-oriented
B. Integrated
, C. Volatile (data is non-volatile once stored)
D. Time-variant
Rationale:-Data warehouses are non-volatile (data is not deleted or overwritten).
Subject-oriented, integrated, and time-variant are key characteristics defined by
Inmon.
Q12. Which of the following is an example of a leading KPI?
A. Customer satisfaction score (lagging)
B. Number of sales calls made per day (leading)
C. Quarterly revenue (lagging)
D. Employee turnover rate (lagging)
Rationale:-Leading KPIs predict future performance (e.g., sales calls, website
visits). Lagging KPIs reflect past performance (revenue, turnover, satisfaction).
Q13. In the CRISP-DM framework, the phase where data is cleaned,
transformed, and prepared for modeling is called:
A. Business understanding
B. Data understanding
C. Data preparation
D. Modeling
Rationale:-CRISP-DM (Cross-Industry Standard Process for Data Mining)
phases: Business Understanding, Data Understanding, Data Preparation,
Modeling, Evaluation, Deployment.
Q14. A company uses a dashboard that refreshes every 15 minutes showing
current production line efficiency. This is an example of:
A. Batch processing
B. Real-time analytics (near real-time)
C. Predictive modeling
D. Prescriptive optimization
Rationale:-Real-time or near-real-time analytics processes data as it arrives to
provide up-to-the-minute insights. Batch processing would be daily or hourly
updates.
Q15. Which of the following best describes the difference between supervised
and unsupervised machine learning?
A. Supervised uses labeled data; unsupervised uses unlabeled data
B. Supervised uses unlabeled data; unsupervised uses labeled data
C. Both require labeled data
