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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Business value and ROI - Data quality tools and technologies |
| Topic 2: Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Integration with data governance - Roles and responsibilities |
| Topic 3: Data Quality Improvement | 20% | - Root cause analysis - Data cleansing and standardization - Monitoring and continuous improvement |
| Topic 4: Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality in DMBOK framework - Data quality dimensions |
| Topic 5: Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Data profiling techniques - Defining quality rules and metrics |
DQ-1220スタディガイドの優れた利点の1つは、高い合格率です。これは99％に達し、同業他社の平均合格率よりもはるかに高くなっています。当社の高い合格率は、当社が業界トップのDQ-1220準備ガイドである理由を説明しています。自信の源は、素晴らしいDQ-1220試験問題です。 DQ-1220学習教材の練習を約20〜30時間続ける限り、試験に合格しても問題はありません。私たちの専門家は、実際の試験問題に合わせてDQ-1220の質問と回答を設計しました。これは、高い能力で試験に合格するのに役立ちます。
質問 # 28
Which of the following is the best example of a 'documented data quality rule'?
正解：E
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 # 29
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
正解：D
解説：
The correct definition is the fundamental organisation of a system, and the principles governing its design and evolution. DAMA-DMBOK2 incorporates the ISO/IEC 42010 architectural concept when establishing the theoretical basis for Data Architecture.
The fuller ISO formulation describes architecture in terms of the fundamental organization of a system embodied in its components, their relationships to one another and to the environment, together with the principles governing design and evolution. DAMA uses this concept to distinguish true architecture from a collection of diagrams or implementation specifications.
Option E is therefore incomplete: architectural artifacts document architecture, but the artifacts themselves are not the architecture. Option C focuses too narrowly on implementation and maintenance, while options B and D describe potential objectives or constraints rather than the definition.
Applied to enterprise data, this means architecture establishes the high-level organization of data assets, information flows, platforms, relationships, and guiding principles by which the data environment evolves.
Data Quality benefits because architectural principles establish where authoritative information originates, how it moves, how duplication is controlled, and where governance and quality controls must operate.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Architecture Definition; ISO/IEC 42010; Enterprise Data Architecture; Architectural Principles; Chapter 13 - Quality by Design.
質問 # 30
Descriptive metadata describes a resource, enabling its identification and retrieval.
正解：C
解説：
DAMA-DMBOK2 distinguishes descriptive, structural, and administrative metadata when discussing metadata classifications used outside conventional IT contexts. Descriptive metadata identifies and describes a resource sufficiently to enable discovery and retrieval. DMBOK2 gives title, author, and subject as explicit examples of descriptive metadata.
A book's author, title, and subject therefore answer practical discovery questions: What is the resource? Who created it? What is it about? These attributes allow catalogues, repositories, search tools, and users to identify and retrieve the required resource.
Option A is principally structural metadata. DMBOK2 specifically identifies the number of pages and chapters as examples describing relationships within or among the components of a resource. Option D represents administrative metadata because version numbers and archive dates help manage the resource throughout its lifecycle.
The distinction is important to Data Quality because metadata itself must have appropriate completeness, consistency, currency, and accuracy. Incorrect descriptive metadata can make a valid data asset effectively undiscoverable or lead users to select an inappropriate source.
Metadata Management therefore supports Data Quality by giving data consumers reliable definitions, context, provenance, ownership, lineage, and usage information.
Reference Topics: DAMA-DMBOK2 Metadata Management - Descriptive Metadata; Structural Metadata; Administrative Metadata; Metadata Quality; Chapter 13 - Data Quality and Metadata Management.
質問 # 31
Profiling reveals that an Age field has a minimum value of -7 and a maximum value of 263. What should the Data Quality team do first?
正解：D
解説：
The values should first be treated as potential exceptions requiring investigation. Profiling identifies anomalies; it does not automatically prove that every unusual value is incorrect or specify the appropriate remediation.
Negative age and 263 years are highly improbable for a conventional person-age attribute, suggesting a validity or reasonableness defect. However, the Data Quality team should confirm the business definition, unit of measure, derivation logic, source mappings, special-value conventions, and intended population before changing the records.
Blindly replacing values with an average introduces fabricated data and destroys traceability. Deleting the records may also remove legitimate information elsewhere in the record. Increasing the datatype range addresses technical storage rather than semantic correctness.
A disciplined Data Quality process moves from profiling to rule validation, issue assessment, root-cause analysis, remediation, and monitoring. The business rule may ultimately specify an acceptable age range, but that rule should be governed and documented rather than inferred from individual anomalies.
Metadata is essential because it should describe whether Age is stored directly, calculated from Date of Birth, or encoded using another convention.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Profiling; Validity; Reasonableness; Root-Cause Analysis; Data Quality Rules; Metadata.
質問 # 32
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
正解：D
解説：
The organization should determine the root cause and authoritative remediation point before multiple teams independently modify downstream copies.
If a defective value originates in a source system and is distributed to five downstream applications, correcting those five copies independently may provide temporary relief but leaves the upstream defect intact.
The incorrect value may simply be redistributed again.
Lineage should be used to trace the defect through source systems, transformations, integration layers, Master Data hubs, warehouses, and reports. Once the origin is understood, governance can determine which system or process is authoritative and where correction should occur.
This approach minimizes inconsistent local fixes and reduces the risk that different teams apply incompatible interpretations of the same issue.
DAMA's Chapter 13 revision explicitly strengthens the relationship between Data Quality and Metadata Management, Reference/Master Data Management, Modeling, and Data Integration. Those connections are precisely what enable cross-system issue resolution.
Downstream correction may still be required after the authoritative source is fixed, but it should be coordinated.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Issue Management; Metadata Lineage; Authoritative Sources; Data Integration.
質問 # 33
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Fast2test10年以上のビジネスエクスペリエンスを備えたDAMAのDQ-1220テストトレントは、顧客の購入エクスペリエンスを非常に重視しました。 電子製品の購入速度を心配する必要はありません。 弊社では、DQ-1220試験準備の信頼性を長期間にわたって評価および評価し、保証された購入スキームを提案するために尽力しています。 必要な場合は、DQ-1220テストトレントを使用するためのリモートオンラインガイダンスも利用できます。 通常、購入後数分でData Quality練習問題を効率よく取得できます。
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