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WGU Data-Driven-Decision-Making Exam Syllabus Topics:

SectionObjectives
Decision-Making Frameworks- Evidence-Based Decision Making
- Cost-Benefit Analysis
- Risk Analysis and Assessment
- Rational Decision Models
Data-Driven Culture and Communication- Ethical Considerations in Data Use
- Presenting Data Insights
- Building Data-Informed Organizations
- Stakeholder Communication
Business Intelligence and Analytics- Key Performance Indicators (KPIs)
- Dashboards and Reporting
- Predictive Analytics Basics
- Data Mining Concepts
Data Analysis Fundamentals- Data Visualization Techniques
- Descriptive Statistics
- Data Quality and Cleaning
- Data Types and Measurement Scales

>> Data-Driven-Decision-Making Real Question <<

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WGU VPC2Data-Driven Decision MakingC207 Sample Questions (Q65-Q70):

NEW QUESTION # 65
A nonprofit organization is asking for donations. It hopes to design an email campaign that will ensure it receives at least $50,000. The campaign will reach 10,000 donors and receive donations with a mean of $10 and a standard deviation of $5.
Which measure should be used to determine the probability of the campaign receiving $50,000?

Answer: A

Explanation:
To determine the probability of achieving a specific monetary threshold, data-driven decision making relies on standardization using the z-score. A z-score measures how many standard deviations a value is from the mean and allows analysts to calculate probabilities using the normal distribution.
In this scenario, the nonprofit wants to assess the likelihood that total donations will reach at least $50,000 given a known mean and standard deviation. The z-score enables conversion of the donation target into a standardized value, which can then be evaluated using probability tables or statistical software.
R-squared measures model fit in regression, the t-statistic is used in hypothesis testing, and the median does not support probability calculations. Therefore, the appropriate measure for determining probability in this context is thez-score, making optionDcorrect.


NEW QUESTION # 66
How should a marketing consulting firm perform a cluster analysis for a new granola bar?

Answer: A

Explanation:
Cluster analysisis an unsupervised learning technique used to group observations based on similarity. In data- driven decision making, it is commonly used formarket segmentation, allowing firms to identify distinct customer groups with similar preferences or behaviors.
For a new granola bar, cluster analysis helps determine which consumer segments exist, such as health- conscious buyers, convenience-focused consumers, or price-sensitive shoppers. This enables targeted marketing strategies and product positioning.
Understanding reasons for purchase requires survey or causal analysis, not clustering. Competitor benchmarking and trend analysis involve different analytical techniques.
Therefore, the correct answer isB, determining different segments or groups to target.


NEW QUESTION # 67
A major hospital system wants to improve its level of patient satisfaction. Management has recently asked patients to complete a survey as part of the discharge process. From this survey, a net promoter score is derived and tracked over time. What does a net promoter score reveal?

Answer: C

Explanation:
A net promoter score reveals the strength of an organization's customer relations by measuring how likely customers are to recommend the organization to others. In a hospital setting, patients are the relevant customer group, and their willingness to recommend the hospital reflects satisfaction, trust, and overall experience. Because the score is tracked over time, management can use it as an indicator of whether customer relationships are improving or declining. A higher net promoter score generally suggests stronger loyalty and more positive perceptions, while a lower score may indicate service or quality concerns. The score is not about how management feels about employees, nor is it used to track employee advancement. It may be influenced indirectly by marketing, but it does not primarily measure advertising campaign effectiveness. Its main purpose is to provide a clear signal about customer loyalty and relationship strength. Therefore, the correct answer is the strength of an organization's customer relations.


NEW QUESTION # 68
Which graphical display is used to examine the distribution of a data set with quartiles?

Answer: A

Explanation:
Aboxplotis specifically designed to display the distribution of a dataset using quartiles. In data-driven decision making, boxplots visually summarize data through the minimum, first quartile, median, third quartile, and maximum.
Boxplots are useful for identifying spread, central tendency, skewness, and potential outliers. Scatterplots and bivariate charts analyze relationships between variables, while Pareto charts rank categorical data by frequency.
Because quartiles are the defining feature of a boxplot, the correct answer isC.


NEW QUESTION # 69
What results from starting an analysis with flawed data?
Choose 2 answers.

Answer: C,D

Explanation:
Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is thatmore time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.
Another critical result is thatmissing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions.
This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.
Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.
Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers areB and D.


NEW QUESTION # 70
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