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| Section | Objectives |
|---|---|
| Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
弊社は、当社のDQ-1220試験エンジンを学習ツールとして使用する方法で、候補者とのさらなる協力を目指して、大きな集中的な進歩を遂げました。専門の研究チームと責任ある作業スタッフの献身により、DQ-1220トレーニング資料は広く認められ、現在ではDQ-1220試験軍隊に参加する人々が増え、私たちはトップクラスのトレーニング資料プロバイダーになりました。国際市場。 DQ-1220の実践教材は、試験に合格するためのタイムリーで効果的な支援になると考えています。
質問 # 66
What is Data Stewardship?
正解：E
解説：
DAMA-DMBOK2 defines Data Stewardship in terms of accountability and responsibility for data and for the processes that ensure its effective control and use. The wording in option E closely matches the DMBOK2 definition: stewardship describes accountability and responsibility for data and processes that ensure the effective control and use of data assets.
Stewardship may be formalized through named positions or responsibilities embedded within existing business roles. Its practical scope commonly includes managing business terminology, defining valid values and business rules, establishing or approving Data Quality requirements, resolving data issues, applying standards, and supporting governance decisions.
Option C is too narrow and also confuses stewardship with the broader policy-setting responsibilities of Data Governance. Stewards participate in developing and implementing policies and standards, but stewardship is not merely a policy-creation role. Similarly, it is not a privacy technology function or a project-management construct.
Within Data Quality Management, Data Stewards are critical because quality must be defined relative to business requirements. They help determine what "fit for purpose" means, establish acceptable thresholds, prioritize defects according to business impact, and participate in root-cause remediation.
Metadata Management records stewardship decisions, while Master Data Management uses those decisions to govern shared enterprise entities.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Steward Responsibilities; Business Glossary; Chapter 13 - Data Quality Governance and Issue Management.
質問 # 67
The Family Name of a Person is recorded in a system. The column name is Pname. Pname is an example of:
正解：D
解説：
Pname is Metadata because it is the name assigned to a database column and therefore describes the structure in which the actual Family Name values are stored. A value such as Smith or Patel would be data; Pname is structural information describing that data.
DAMA-DMBOK2 defines Metadata broadly as information about data and data-related processes. Technical metadata includes physical database characteristics such as table names, column names, datatypes, lengths, indexes, constraints, and related structural properties. The interpretation of Pname as metadata is also consistent with the published version of this exact certification question.
The fact that Pname may be an unclear abbreviation does not change its classification. It may represent a poor naming convention from a usability or governance perspective, but technically it remains metadata. A stronger physical name might be FamilyName, while the metadata repository could additionally map that field to the authoritative business glossary term.
This distinction matters to Data Quality because rules are frequently attached to metadata objects. For example, the Family Name attribute might have completeness, permitted-character, maximum-length, and standardization requirements.
Effective Metadata Management connects the technical column to its business meaning, system lineage, stewardship, and applicable Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 11 - Technical Metadata; Database Metadata; Business Glossary; Chapter 13 - Data Quality Rules and Metadata.
質問 # 68
When a data quality team has more issues than they can manage, they should look to:
正解：E
解説：
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
質問 # 69
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
正解：E
解説：
An enterprise Master Data Repository serving multiple applications is a classic Hub-and-Spoke integration pattern. In this architecture, shared information is consolidated physically or virtually in a central hub, while participating applications interact with that hub rather than building a separate direct interface to every other application.
DAMA-DMBOK2 explicitly identifies Master Data Management hubs, Data Warehouses, Data Marts, and Operational Data Stores as familiar examples of data hubs. The hub-and-spoke model reduces the proliferation of point-to-point interfaces and can provide a consistent enterprise view of shared data.
This architecture is particularly appropriate for MDM because customer, product, supplier, location, or other master entities often need to be standardized, matched, governed, and redistributed across many systems. The central hub can apply survivorship rules, reference mappings, stewardship decisions, and quality controls before distributing trusted values.
Replication and Change Data Capture are techniques for moving or detecting changes in data, but neither describes the overall interaction topology. Publish-subscribe may operate alongside an MDM hub as a distribution mechanism, but the enterprise repository itself represents the hub within a hub-and-spoke architecture.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Hub-and-Spoke; Integration Interaction Models; Chapter
10 - Master Data Management; Enterprise Master Data Hub; Data Quality Consistency.
質問 # 70
A customer is shown as "Active" in the CRM but "Closed" in the billing system at the same point in time.
Both systems use the same customer identifier. Which Data Quality dimension is primarily affected?
正解：B
解説：
The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.
The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation.
However, unless a legitimate business rule explains the difference, the two representations are inconsistent.
Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of
"Active" and "Closed," examine synchronization timing, and trace transformation or integration logic.
Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.
Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.
DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.
質問 # 71
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