クライアントにDQ-1220学習準備で一流のサービスを提供します。これには、販売の前後のサービス、24時間のオンラインカスタマーサービスと長距離支援、返金サービス、更新サービスが含まれます。クライアントは、私たちの試してみると、クライアントは、彼らはいつでも私たちの顧客サービスに連絡することができ、販売後の私たちの製品について問題がある場合は、販売前に自由にDQ-1220ガイド素材をダウンロードすることができます。私たちDAMAは、Data Qualityトレーニングクイズについて、クライアントの質問や疑問を返信し、その問題を解決するため、24時間オンライン顧客サービスを提供しています。
DAMA DQ-1220（データ品質）試験は、データ品質管理の分野での候補者の熟練度をテストするために設計された認証プログラムです。この試験は、データプロファイリング、データクレンジング、データ標準化、データ監査、およびデータガバナンスなど、データ品質に関連する広範なトピックをカバーしています。この認証は、データ管理分野で働き、自己組織内でのデータ品質を確保する責任を持つプロフェッショナルにとって理想的な認証です。
Topexamあなたは自分の仕事の能力が認められない、またはあなたが長い間昇進していないと不満を言うかもしれません。ただし、DQ-1220試験に合格しようとすると、高収入で良い仕事を見つける可能性が高くなります。そのため、DQ-1220の質問トレントを購入することをお勧めします。 DQ-1220試験の教材を購入して学習すると、試験に合格してより良い仕事を得るための簡単なものであることがわかります。購入前にDQ-1220試験問題の概要を注意深くお読みください。私たちはあなたに最高のサービスを提供し、あなたが満足することを願っています。
DAMA DQ-1220試験を受ける資格を得るには、候補者はデータ管理で働いた少なくとも2年の経験が必要です。また、データ品質のコースまたはトレーニングプログラムを完了したか、同等の知識と経験を持っている必要があります。この試験は、オンラインまたはテストセンターで利用できます。
DAMA DQ-1220認定試験は、データ品質の専門家の能力を測定するための標準として、世界的に認められています。この総合的な試験は、データ品質管理で使用される原則、技術、方法論の理解力を評価します。試験は、候補者がデータ品質の問題を特定して解決し、データ品質計画を開発および実施し、データ品質イニシアチブの効果を測定する能力を評価するように設計されています。DAMA DQ-1220認定試験に合格することは、候補者が組織のデータが正確で信頼できることを保証するために必要な知識とスキルを持っていることを示します。
質問 # 111
What is the purpose of Data Governance?
正解：B
解説：
The precise DAMA-DMBOK2 purpose of Data Governance is to ensure that data is managed properly according to policies and best practices. DMBOK2 distinguishes this governance purpose from the broader objective of Data Management, which is concerned with obtaining value from data throughout its lifecycle.
Governance establishes the authority and control framework within which operational data-management functions work. This includes strategy, policy, standards, accountability, stewardship, compliance, issue escalation, quality oversight, and decision rights. DMBOK2 specifically identifies governance responsibilities involving policies for metadata, access, security and quality; setting Data Quality and Data Architecture standards; and providing oversight and corrective action.
This distinction is critical for Data Quality. Data Quality Management performs activities such as profiling, measurement, root-cause analysis, cleansing and monitoring. Data Governance determines who has authority, which requirements are mandatory, what thresholds are acceptable, which issues require escalation, and who owns remediation decisions.
Metadata Management supports governance by recording authoritative definitions, lineage, ownership and quality rules. Master Data Management operationalizes governance for shared business entities and reference domains.
Options B, C and D describe legitimate aspects of wider Data Management, but they do not state the specific purpose assigned to Data Governance by DMBOK2.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Introduction; Goals and Principles; Governance Oversight; Chapter 13 - Data Quality and Governance.
質問 # 112
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
正解：D
解説：
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
質問 # 113
Achieving security risk reduction in an organisation begins with developing what?
正解：E
解説：
Effective security risk reduction begins by understanding what data exists, how sensitive or critical it is, and where it resides. A classification model provides this foundation by classifying data concepts according to protection requirements and linking those concepts to their physical implementations. The certification material reflects this DAMA principle directly.
Data cannot be protected proportionately if the organization does not know whether it represents public information, internal operational information, personally identifiable information, financial data, intellectual property, regulated information, or another sensitive category. Classification allows the organization to apply appropriate controls according to risk rather than treating every data asset identically.
Once classification is established, security teams can define access requirements, encryption needs, monitoring, retention controls, masking, authorization policies, and other safeguards. Metadata is essential because classifications must ultimately be connected to actual databases, files, attributes, interfaces, and repositories.
A firewall alone does not provide data-level protection, and role classification by itself addresses only one dimension of security. Similarly, an Enterprise Data Model describes organizational data structures but does not replace security classification.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Data Classification; Risk Reduction; Sensitive Data Discovery; Metadata Management; Chapter 13 - Integrity and Controlled Use.
質問 # 114
Examples of transformation include:
正解：E
解説：
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
質問 # 115
Well executed metadata management enables:
正解：A
解説：
The fundamental outcome of well-executed Metadata Management is a consistent understanding of data resources. DAMA-DMBOK2 states this directly: effective Metadata Management enables consistent understanding of data resources and supports more efficient development across organizational boundaries.
Metadata supplies context that raw data cannot provide by itself. It explains business meaning, technical structures, valid domains, calculations, transformations, ownership, lineage, systems of record, access restrictions, known quality issues, and update schedules. DMBOK2 also identifies Data Quality rules and measurement results as forms of business metadata.
Consequently, Metadata Management is broader than database design, dimensional reporting, authorization, or Data Modeling. Each of those activities consumes metadata, but none represents its complete objective.
This capability is essential to Data Quality. A dataset cannot be meaningfully evaluated for fitness for purpose unless users know what its fields mean, where the data originated, how values were transformed, which business rules apply, and which source is authoritative. Metadata therefore provides the semantic and technical framework required to define and interpret Data Quality measurements consistently.
DAMA's current DMBOK2 revision also explicitly strengthens the relationship between Data Quality and Metadata Management.
Reference Topics: DAMA-DMBOK2 Metadata Management - Goals and Principles; Business Metadata; Technical Metadata; Data Lineage; Chapter 13 - Data Quality and Metadata Management.
質問 # 116
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DQ-1220試験感想: https://www.topexam.jp/DQ-1220_shiken.html