WGU Data-Driven-Decision-Making Exam | Data-Driven-Decision-Makingファンデーション - 無料ダウンロード Data-Driven-Decision-Making資格問題集 なん時でもテストData-Driven-Decision-Making認定の取得は、学習プロセスの目標を達成するために必要であり、労働者のために働いており、開発のためのより広いスペースを提供できるより多くの資格を持っています。 Data-Driven-Decision-Makingの実際の試験ガイドは、効率的で便利な学習プラットフォームを提供するため、できるだけ早く認定を取得できます。高い学位は能力の表れかもしれません。テストData-Driven-Decision-Making認定を取得することも良い選択です。 Data-Driven-Decision-Making証明書を取得すると、より良い未来を創造するための選択肢が増えます。 WGU VPC2Data-Driven Decision MakingC207 認定 Data-Driven-Decision-Making 試験問題 (Q25-Q30):質問 # 25
What is the basic difference between evaluating costs and benefits in the public and private sectors?
A. The benefits of public projects are easily quantifiable.
B. Private projects generate considerable revenue.
C. The costs associated with public projects are minimal.
D. The benefit of private projects is general public welfare.
正解：B
解説：
The fundamental difference between cost-benefit evaluation in the public and private sectors lies inhow benefits are defined and measured. In data-driven decision making, private-sector projects primarily focus onrevenue generation and profitability, making optionCthe correct distinction.
Private organizations evaluate benefits using measurable financial outcomes such as revenue, profit margins, and return on investment. These metrics provide clear, quantifiable indicators of success. In contrast, public- sector projects often aim to maximizegeneral public welfare, including social, environmental, and economic benefits that are more difficult to quantify monetarily.
Public-sector benefits may include improved public health, safety, education, or trust in government- outcomes that do not translate directly into revenue. Therefore, while costs are measurable in both sectors, benefits differ substantially in nature.
Options A and B are incorrect because public-sector costs are not minimal and public benefits are often difficult to quantify. Option D incorrectly assigns public welfare to private projects. Thus, the correct answer isC.
質問 # 26
What is the primary goal of Six Sigma?
A. Furthering a commitment to the SIPOC process
B. Fostering a commitment to continuous improvement
C. Demonstrating strong management leadership
D. Providing collaborative planning, forecasting, and replenishment
正解：B
解説：
The primary goal ofSix Sigmais tofoster a commitment to continuous improvementby systematically reducing defects and process variation. In data-driven decision making, Six Sigma uses statistical methods to improve quality, efficiency, and consistency across organizational processes.
Six Sigma emphasizes disciplined problem-solving through data analysis, root-cause identification, and process control. While reducing defects to 3.4 per million opportunities is a hallmark metric, the broader objective is embedding continuous improvement into organizational culture.
SIPOC is a supporting tool, leadership is a contributing factor, and collaborative planning forecasting and replenishment relates to supply chain management, not Six Sigma's core purpose.
Therefore, the correct answer isD.
質問 # 27
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
A. It guarantees that a sample will be statistically significant.
B. It begins the statistical process faster.
C. It mitigates undetected errors from the data-entry process.
D. It ensures there are no missing data points.
正解：C、D
解説：
Good data quality management plays a critical role in improving business decision-making by ensuring that data is accurate, complete, and reliable. One key benefit is that itensures there are no missing data points, which helps maintain data completeness. Missing data can distort results, reduce analytical power, and lead to incorrect conclusions, especially in descriptive and inferential statistics.
Another important benefit is that data quality managementmitigates undetected errors from the data-entry process. Errors such as duplicate entries, incorrect values, or inconsistent formats can significantly bias analysis if left unnoticed. Through validation checks, cleaning procedures, and governance standards, organizations reduce the risk of flawed insights.
While good data quality supports better analysis, it does not guarantee statistical significance, as significance depends on sample size, variability, and study design. Similarly, it does not necessarily make the statistical process faster; in fact, data cleaning can be time-consuming. However, it improves theaccuracy and trustworthinessof outcomes.
In data-driven decision making, high-quality data is essential because decisions are only as good as the data used to support them. Therefore, the correct answers areA and D.
質問 # 28
What is the purpose of the quality management principle of dedication to fact-based decision-making?
A. Eliminate anything that does not add value.
B. Increase loyalty from customers and suppliers.
C. Increase the effectiveness from quality practices.
D. Reduce bias driven by increased trust in plans.
正解：D
解説：
The principle offact-based decision-makingemphasizes using reliable data and objective analysis rather than intuition or opinion. In data-driven decision making, this principle exists primarily toreduce bias and increase trust in organizational plans and decisions.
When decisions are grounded in verified data, assumptions are challenged, personal biases are minimized, and outcomes are more predictable. This builds confidence among stakeholders and supports transparency and accountability.
Customer loyalty, waste elimination, and quality effectiveness may be indirect benefits, but the core purpose is ensuring that decisions are objective, defensible, and evidence-based. Therefore, the correct answer isD.
質問 # 29
For which situation could a scatter diagram be used?
A. Demonstrating a significant difference between the means of two groups of data
B. Demonstrating a visual precedence of a prioritization matrix
C. Demonstrating a relationship between variables
D. Demonstrating a significant difference between the frequencies of two groups of data
正解：C
解説：
Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data- driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.
Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.
A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.