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質問 # 74
A Data Quality team finds that 78% of customer complaints are caused by four defect categories out of twenty recorded categories. Which tool is most appropriate for prioritizing improvement activity?
正解：B
解説：
Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.
The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.
The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship.
Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.
Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.
Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.
質問 # 75
A team uses a fishbone diagram during investigation of recurring missing customer email addresses. What is the primary purpose of the diagram?
正解：A
解説：
A fishbone diagram, also known as an Ishikawa or cause-and-effect diagram, is used to organize and explore potential root causes of a problem.
For recurring missing email addresses, categories might include People, Process, Technology, Data, Policy, Training, and External Sources. Potential causes could include optional form design, unclear business requirements, API mapping defects, missing supplier data, or inconsistent onboarding procedures.
The technique helps prevent teams from prematurely assuming that the most visible symptom is the actual cause. It encourages structured investigation across multiple dimensions of the process.
Once hypotheses are identified, evidence should be collected to confirm or reject them. The diagram itself does not prove causation.
In Data Quality programs, root-cause analysis is essential because repeated cleansing without process correction allows defects to recur. DAMA's Chapter 13 revision explicitly retains and strengthens the treatment of common causes and the improvement lifecycle.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Cause-and-Effect Diagram; Issue Management; Continuous Improvement.
質問 # 76
An organization changes the definition of "Net Revenue". Before implementing the change, it wants to identify every report, transformation, and analytical model that depends on the existing definition. Which capability is most important?
正解：B
解説：
The required capability is Metadata lineage and impact analysis. Changing a business definition can affect far more than the glossary entry itself. The organization needs to know which physical fields, transformation rules, ETL jobs, calculations, reports, dashboards, analytical models, and downstream datasets depend on the current definition.
Lineage shows where data originates and how it flows and transforms. Impact analysis follows those relationships forward to determine what will be affected by a proposed change.
This prevents a common Data Quality failure in which a definition changes in one area while downstream systems continue applying the old interpretation. The result can be technically correct calculations that are semantically inconsistent.
A governed change should therefore update the business glossary, transformation metadata, Data Quality rules, reporting definitions, and affected documentation in a coordinated manner. Appropriate Data Stewards and Data Owners should approve the new meaning and effective date.
DAMA's framework describes Metadata Management as enabling understanding through definitions, lineage, and usage across systems, while the revised Chapter 13 explicitly strengthens the Data Quality-Metadata relationship.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Impact Analysis; Business Glossary; Data Quality Consistency.
質問 # 77
A goal of reference and master data management is for data to ensure shared data is:
正解：E
解説：
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
質問 # 78
Which of the following is the best example of a 'documented data quality rule'?
正解：A
解説：
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
質問 # 79
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