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| Section | Objectives |
|---|---|
| Topic 1: Data Analysis Fundamentals | - Data Types and Measurement Scales - Data Quality and Cleaning - Data Visualization Techniques - Descriptive Statistics |
| Topic 2: Decision-Making Frameworks | - Rational Decision Models - Cost-Benefit Analysis - Evidence-Based Decision Making - Risk Analysis and Assessment |
| Topic 3: Data-Driven Culture and Communication | - Building Data-Informed Organizations - Ethical Considerations in Data Use - Presenting Data Insights - Stakeholder Communication |
| Topic 4: Business Intelligence and Analytics | - Key Performance Indicators (KPIs) - Predictive Analytics Basics - Dashboards and Reporting - Data Mining Concepts |
>> Data-Driven-Decision-Making学習教材 <<
VPC2Data-Driven Decision MakingC207 Data-Driven-Decision-Makingは、技術的な精度の最高水準を高め、認定された主題と専門家のみを使用します。最新の正確なData-Driven-Decision-Making試験トレントをクライアントに提供し、提供する質問と回答は実際の試験に基づいています。合格率が高く、約98％-100％であることをお約束します。また、Data-Driven-Decision-Makingテストブレインダンプは高いヒット率を高め、試験を刺激してData-Driven-Decision-Making試験の準備を整えることができます。あなたの成功は、Data-Driven-Decision-Making試験問題に縛られています。
質問 # 107
Which two characteristics must a researcher consider concerning data quality when ensuring that an analysis is based on a clean data set?
Choose 2 answers.
正解：A、C
解説：
When evaluating whether a data set is clean enough for analysis, a researcher must focus on data quality dimensions that directly affect validity and usefulness. Two important characteristics are uniqueness and relevance. Data elements must be unique to prevent duplicate records from distorting counts, averages, totals, and trend analyses. Duplicate entries can lead to biased results, especially in customer, transaction, or survey data. Relevance is equally important because even accurate data are not helpful if they do not pertain to the question being studied. A clean data set should support the actual purpose of the analysis rather than merely being complete or large. The statement about age is incorrect because timeliness often matters; outdated data may no longer reflect the current environment. The statement that data cannot contain outliers is also too absolute. Outliers may be valid observations and can sometimes reveal important conditions, anomalies, or data-entry problems that require investigation rather than automatic removal. Thus, the best two characteristics are uniqueness and relevance, because both directly support meaningful, accurate, and decision- ready analysis.
質問 # 108
A student with a degree is presumed to already have a bachelor's degree. Which type of data does this represent?
正解：A
解説：
This question refers to categorizing information rather than measuring it numerically. The phrase "a student with a degree" identifies a classification or label, not a value with mathematical meaning. Nominal data are used to place observations into distinct categories without any inherent numerical order or ranking. In this case, the student is being grouped according to degree status, which is a named category. Interval and ratio data are numerical measurement scales, so they do not apply here. Ordinal data involve ranked categories, such as low, medium, and high, or freshman through senior, where order matters. Here, the information does not describe rank or position; it simply identifies a class of person based on a characteristic. Even though the phrase mentions a bachelor's degree, the key issue is that the information is categorical rather than numeric.
Therefore, this is best understood as nominal data. In data analysis, recognizing nominal variables is important because they are usually summarized with counts, percentages, or category-based comparisons rather than means or standard deviations.
質問 # 109
A manager has been assigned to manage a digital marketing analytics team. The manager tasks the team with determining similarities among existing customers in the company's database, such as similarities in products purchased, location, and the average amount spent per order among existing customers.
Which type of activity will help the team accomplish this task?
正解：A
解説：
Data miningis the appropriate activity for identifying patterns, similarities, and relationships within large datasets. In data-driven decision making, data mining techniques such as clustering and association analysis are commonly used to segment customers based on behavior and characteristics.
The task described involves uncovering hidden patterns across multiple variables, which aligns directly with data mining objectives. Linear programming focuses on optimization, regression predicts outcomes, and touchpoint analysis examines customer interactions rather than similarities.
Therefore, the correct answer isA, data mining.
質問 # 110
Which distribution would have a mean and median that are approximately equal after an analysis of each employee's number of customer service calls over the last month?
正解：C
解説：
In anormal distribution, data is symmetrically distributed around the center, causing the mean and median to be approximately equal. This is a fundamental property emphasized in data-driven decision making.
Skewed distributions, such as Pareto, cause the mean and median to differ significantly. Therefore, the correct answer isA.
質問 # 111
Which element is associated with control charts?
正解：B
解説：
Control charts are a core tool in statistical process control and are used to monitor variation in a process over time. One of their defining features is the inclusion of upper and lower control limits. These limits help determine whether process variation is consistent with common-cause variation or whether unusual, assignable causes may be affecting performance. The chart typically includes a center line representing the process average, along with upper and lower limits that establish the acceptable range of variation under stable conditions. When points fall outside these limits or show nonrandom patterns, the process may require investigation. While hypothesis testing and correlation are important statistical concepts, they are not the primary identifying elements of a control chart. A reliability index may be used in certain engineering or quality contexts, but it is not the standard feature that defines control charts. Therefore, the element most directly associated with control charts is upper and lower limits, since those boundaries are central to evaluating whether a process remains in statistical control over time.
質問 # 112
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