WGU Data Driven Decision Making C207 EXAM STUDY GUIDE 2026/2027 Accurate Questions with Correct Detailed Answers || Newest Version

WGU Data Driven Decision Making C207
Exam Study Guide 2026/2027 Accurate
Questions with Correct Detailed Answers
|| 100% Guaranteed Pass – Newest
Version


WGU C207 Data-Driven Decision Making Exam Study Guide 2026/2027
100 Accurate Questions with Correct Detailed Answers

Question 1: Which term refers to the discovery, analysis, and communication of
meaningful patterns in data?
A) Statistics
B) Analytics ✅
C) Data Mining
D) Management Science

Detailed Rationale: Analytics is the overarching process of discovering, interpreting,
and communicating meaningful patterns in data to inform decision-making. Statistics is
a tool used within analytics, while data mining is a specific technique for discovering
patterns in large datasets.

Question 2: A manufacturer wants to maximize factory output while specifically
minimizing labor costs. What type of analytics might they employ to achieve this goal?
A) Descriptive Analytics
B) Diagnostic Analytics

,C) Predictive Analytics
D) Prescriptive Analytics ✅

Detailed Rationale: Prescriptive analytics is used to recommend an optimal course of
action by evaluating multiple scenarios and constraints. This aligns with the goal of
maximizing output while minimizing a specific cost (labor).

Question 3: What is the first step in the Davenport-Kim Three-Stage Model of decision-
making?
A) Solving the problem
B) Communicating results
C) Framing the problem ✅
D) Deploying the model

Detailed Rationale: The Davenport-Kim model consists of three stages: 1) Framing the
problem (identifying the objective and scope), 2) Solving the problem (data analysis and
modeling), and 3) Communicating results (presenting insights). Framing is the critical
first step.

Question 4: The cleaning and organizing of collected raw data refers to which step in
the Data Analytics Lifecycle?
A) Data Acquisition
B) Data Preprocessing ✅
C) Modeling
D) Deployment

Detailed Rationale: Data preprocessing (or cleaning) involves handling missing data,
outliers, duplicates, and inconsistent formatting to prepare raw data for analysis. It
occurs after data acquisition and before exploratory data analysis (EDA) or modeling.

Question 5: Which level of measurement classifies data into categories that cannot be
ordered?
A) Nominal ✅

,B) Ordinal
C) Interval
D) Ratio

Detailed Rationale: Nominal data are categorical labels with no logical order (e.g.,
types of cars, gender, colors). Ordinal data have order, while interval and ratio data have
meaningful numerical values.

Question 6: Olympic medals of gold, silver, and bronze are examples of which type of
data?
A) Nominal
B) Ordinal ✅
C) Interval
D) Ratio

Detailed Rationale: Ordinal data represents categories with a meaningful order or rank,
but the intervals between the categories are not necessarily equal. Gold, silver, and
bronze have a clear ranking (1st, 2nd, 3rd).

Question 7: What is a key characteristic of Ratio data that distinguishes it from Interval
data?
A) Data are categorical
B) Data have order but unequal intervals
C) Data have a true absolute zero ✅
D) Data are ranks

Detailed Rationale: The key difference is that ratio data has a true zero point (e.g.,
height, weight, income, number of cars), allowing for meaningful ratios. Interval data
(e.g., temperature in Celsius) has equal intervals but no true zero.

Question 8: Data that can only take on whole values and has clear boundaries (e.g.,
number of cars in a parking lot) is known as:
A) Continuous Data

, B) Ordinal Data
C) Discrete Data ✅
D) Nominal Data

Detailed Rationale: Discrete data can only take specific, separate values (usually
integers) and cannot be meaningfully subdivided. Continuous data can take any value
within a range.

Question 9: What does a histogram display?
A) The relationship between two variables
B) The distribution of continuous data ✅
C) The proportion of a whole
D) The trend over time

Detailed Rationale: A histogram uses adjacent bars to show the frequency distribution
of a continuous variable, dividing the data into bins or ranges.

Question 10: Which measure of central tendency is most appropriate for a highly
skewed distribution, such as income data?
A) Mean
B) Median ✅
C) Mode
D) Range

Detailed Rationale: The median is resistant to outliers, making it the best measure of
central tendency for skewed data. The mean is pulled toward the tail of the skew,
misrepresenting the "typical" value.

Question 11: Which measure of dispersion is robust to outliers, as it focuses on the
spread of the middle 50% of the data?
A) Range
B) Variance