WGU C207 DATA-DRIVEN DECISION-MAKING EXAM
2026/2027 | Latest Update | Questions & Verified Answers
100% Correct | Grade A | Pass Guaranteed - A+ Graded
Section 1: Analytics Types & Business Intelligence (Questions 1-
10)
Question 1 A retail company analyzes last quarter's sales data to determine which
products had the highest revenue. This is an example of:
A. Predictive analytics
B. Prescriptive analytics
C. Descriptive analytics [CORRECT]
D. Diagnostic analytics
Rationale: Descriptive analytics answers "what happened" by summarizing historical
data. Predictive (Option A) forecasts future outcomes. Prescriptive (Option B)
recommends actions. Diagnostic (Option D) explains why something happened. WGU
C207 emphasizes matching analytics type to business question.
Question 2 A hospital uses patient readmission data to identify which factors (age,
comorbidities, length of stay) explain why patients return within 30 days. This
represents:
A. Descriptive analytics
B. Diagnostic analytics [CORRECT]
,C. Predictive analytics
D. Prescriptive analytics
Rationale: Diagnostic analytics answers "why did it happen" by identifying root
causes and relationships. Descriptive (Option A) only summarizes. Predictive (Option
C) would forecast which patients are at risk. Prescriptive (Option D) would
recommend interventions to prevent readmissions.
Question 3 A bank develops a machine learning model to estimate the probability
that a loan applicant will default. This is:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics [CORRECT]
D. Prescriptive analytics
Rationale: Predictive analytics uses historical data and statistical models to forecast
future outcomes (default probability). Descriptive (Option A) and diagnostic (Option
B) look backward. Prescriptive (Option D) would recommend whether to approve the
loan and at what terms.
Question 4 A logistics company uses optimization algorithms to determine the most
cost-effective delivery routes given fuel costs, traffic patterns, and delivery windows.
This is:
A. Predictive analytics
B. Prescriptive analytics [CORRECT]
C. Descriptive analytics
D. Diagnostic analytics
,Rationale: Prescriptive analytics answers "what should we do" using optimization
and simulation to recommend decisions. Predictive (Option A) might forecast
demand but not optimize routes. Descriptive (Option C) and diagnostic (Option D)
do not provide actionable recommendations.
Question 5 A KPI dashboard showing daily website traffic, conversion rates, and
average order value is an example of:
A. Data lake
B. Business Intelligence [CORRECT]
C. Monte Carlo simulation
D. Hypothesis testing
Rationale: Business Intelligence involves technologies and practices for collecting,
analyzing, and presenting business data to support decision-making. KPI dashboards
are core BI tools. Data lake (Option A) is raw storage. Monte Carlo (Option C) is
simulation. Hypothesis testing (Option D) is statistical inference.
Question 6 In OLAP analysis, moving from summarized data to detailed data (e.g.,
from annual sales to monthly sales to individual transactions) is called:
A. Slicing
B. Dicing
C. Drilling down [CORRECT]
D. Pivoting
Rationale: Drilling down navigates from aggregated to detailed data levels. Slicing
(Option A) filters one dimension. Dicing (Option B) selects subcubes across multiple
dimensions. Pivoting (Option D) rotates data axes for different perspectives. OLAP
enables multidimensional data exploration.
, Question 7 A data mart differs from a data warehouse in that a data mart:
A. Contains raw, unprocessed data from all organizational sources
B. Is a subset of data focused on a specific department or business line [CORRECT]
C. Requires no ETL process
D. Stores only real-time streaming data
Rationale: A data mart is a subject-specific subset of a data warehouse (e.g., sales,
marketing, finance). Data warehouse (Option A) is enterprise-wide. Both require ETL
(Option C is incorrect). Data lakes (Option D) handle streaming data. Data marts
improve query performance for specific users.
Question 8 The ETL process in data warehousing stands for:
A. Evaluate, Test, Load
B. Extract, Transform, Load [CORRECT]
C. Enter, Transfer, Link
D. Examine, Tabulate, List
Rationale: ETL = Extract (from source systems), Transform (clean, standardize,
aggregate), Load (into data warehouse). This process ensures data quality and
consistency for analysis. Options A, C, and D are distractors with plausible-sounding
terms.
Question 9 A company stores unstructured data (social media posts, images, sensor
data) in its original format for future analysis. This storage approach is called:
A. Data warehouse
B. Data mart
2026/2027 | Latest Update | Questions & Verified Answers
100% Correct | Grade A | Pass Guaranteed - A+ Graded
Section 1: Analytics Types & Business Intelligence (Questions 1-
10)
Question 1 A retail company analyzes last quarter's sales data to determine which
products had the highest revenue. This is an example of:
A. Predictive analytics
B. Prescriptive analytics
C. Descriptive analytics [CORRECT]
D. Diagnostic analytics
Rationale: Descriptive analytics answers "what happened" by summarizing historical
data. Predictive (Option A) forecasts future outcomes. Prescriptive (Option B)
recommends actions. Diagnostic (Option D) explains why something happened. WGU
C207 emphasizes matching analytics type to business question.
Question 2 A hospital uses patient readmission data to identify which factors (age,
comorbidities, length of stay) explain why patients return within 30 days. This
represents:
A. Descriptive analytics
B. Diagnostic analytics [CORRECT]
,C. Predictive analytics
D. Prescriptive analytics
Rationale: Diagnostic analytics answers "why did it happen" by identifying root
causes and relationships. Descriptive (Option A) only summarizes. Predictive (Option
C) would forecast which patients are at risk. Prescriptive (Option D) would
recommend interventions to prevent readmissions.
Question 3 A bank develops a machine learning model to estimate the probability
that a loan applicant will default. This is:
A. Descriptive analytics
B. Diagnostic analytics
C. Predictive analytics [CORRECT]
D. Prescriptive analytics
Rationale: Predictive analytics uses historical data and statistical models to forecast
future outcomes (default probability). Descriptive (Option A) and diagnostic (Option
B) look backward. Prescriptive (Option D) would recommend whether to approve the
loan and at what terms.
Question 4 A logistics company uses optimization algorithms to determine the most
cost-effective delivery routes given fuel costs, traffic patterns, and delivery windows.
This is:
A. Predictive analytics
B. Prescriptive analytics [CORRECT]
C. Descriptive analytics
D. Diagnostic analytics
,Rationale: Prescriptive analytics answers "what should we do" using optimization
and simulation to recommend decisions. Predictive (Option A) might forecast
demand but not optimize routes. Descriptive (Option C) and diagnostic (Option D)
do not provide actionable recommendations.
Question 5 A KPI dashboard showing daily website traffic, conversion rates, and
average order value is an example of:
A. Data lake
B. Business Intelligence [CORRECT]
C. Monte Carlo simulation
D. Hypothesis testing
Rationale: Business Intelligence involves technologies and practices for collecting,
analyzing, and presenting business data to support decision-making. KPI dashboards
are core BI tools. Data lake (Option A) is raw storage. Monte Carlo (Option C) is
simulation. Hypothesis testing (Option D) is statistical inference.
Question 6 In OLAP analysis, moving from summarized data to detailed data (e.g.,
from annual sales to monthly sales to individual transactions) is called:
A. Slicing
B. Dicing
C. Drilling down [CORRECT]
D. Pivoting
Rationale: Drilling down navigates from aggregated to detailed data levels. Slicing
(Option A) filters one dimension. Dicing (Option B) selects subcubes across multiple
dimensions. Pivoting (Option D) rotates data axes for different perspectives. OLAP
enables multidimensional data exploration.
, Question 7 A data mart differs from a data warehouse in that a data mart:
A. Contains raw, unprocessed data from all organizational sources
B. Is a subset of data focused on a specific department or business line [CORRECT]
C. Requires no ETL process
D. Stores only real-time streaming data
Rationale: A data mart is a subject-specific subset of a data warehouse (e.g., sales,
marketing, finance). Data warehouse (Option A) is enterprise-wide. Both require ETL
(Option C is incorrect). Data lakes (Option D) handle streaming data. Data marts
improve query performance for specific users.
Question 8 The ETL process in data warehousing stands for:
A. Evaluate, Test, Load
B. Extract, Transform, Load [CORRECT]
C. Enter, Transfer, Link
D. Examine, Tabulate, List
Rationale: ETL = Extract (from source systems), Transform (clean, standardize,
aggregate), Load (into data warehouse). This process ensures data quality and
consistency for analysis. Options A, C, and D are distractors with plausible-sounding
terms.
Question 9 A company stores unstructured data (social media posts, images, sensor
data) in its original format for future analysis. This storage approach is called:
A. Data warehouse
B. Data mart
