DQ-1220試験に合格して関連認定を取得する場合、試験の準備をするための信頼できる試験ツールを見つける必要があります。それが、DQ-1220準備ガイドをお勧めしたい理由です。これがあなたが探しているものだと信じているからです。さらに、データ保護法を提供し、DQ-1220ガイド急流を購入した後、ウイルスの侵入や情報漏えいに悩まされないことをDAMA保証します。最後になりましたが、ダウンロードと分割払いに関するガイダンスをリモートで提供するData Quality専門家グループがあります。
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。
地下鉄でほかの人はぽかんと座っているとき、あなたはPadまたはスマホでPDF版のDAMAのDQ-1220試験の資料を読むことができます。ほかの人がインタネットでゲームを遊んでいるとき、あなたはオンラインでDAMAのDQ-1220の問題集をすることができます。このような努力しているあなたは短い時間でDAMAのDQ-1220試験に合格できると信じています。ほかの人はあなたの成績に驚いているとき、ひょっとしたら、あなたはよりよい仕事を探しましたかもしれません。
DAMA DQ-1220試験は、データ管理の分野で働くすべての人にとって重要な認証です。今日のビジネスの世界でデータの重要性が高まっているため、組織はデータの正確性と信頼性を確保できる専門家を探しています。この認定を取得することにより、専門家はこの分野の専門知識を実証し、組織に価値を高めることができます。
DAMA DQ-1220認定試験は、データ品質管理の概念と実践に関する候補者の理解をテストする厳格で包括的な試験です。この試験は、多肢選択式の質問で構成され、データプロファイリング、データクレンジング、データガバナンス、データ品質測定、データ品質の改善など、さまざまなトピックをカバーしています。この試験は、現実世界のシナリオに知識を適用する候補者の能力を評価し、データ品質管理に関連する問題を解決するように設計されています。
質問 # 41
What is the final step in the development of a business-data-driven roadmap?
正解：E
解説：
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
質問 # 42
A business driver for the creation of a Data Quality Management program is:
正解：D
解説：
A primary business driver for establishing a Data Quality Management program is to reduce the risks and costs associated with poor-quality data. DAMA-DMBOK2 treats low-quality data as a direct source of operational cost and organizational risk. The Chapter 13 business drivers include increasing the value of organizational data, reducing risks and costs caused by poor quality, improving efficiency and productivity, and protecting organizational reputation.
Poor-quality data generates both visible and hidden costs. Examples include rework, manual correction, incorrect invoices, failed customer interactions, regulatory exposure, inappropriate decisions, integration delays, missed opportunities, and reputational damage. DMBOK2 further emphasizes that low-quality data represents cost and risk rather than value, making Data Quality management necessary throughout the data lifecycle.
A formal DQ program addresses these consequences through defined requirements, profiling, measurement, root-cause analysis, corrective and preventive action, monitoring, and governance. The purpose is therefore broader than making the DQ team efficient or supporting a specific technology initiative such as warehouse consolidation.
The economic and risk-reduction argument also provides the strongest basis for executive sponsorship because improvements can be linked directly to measurable business outcomes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Business Drivers; Costs of Poor Data Quality; Risk Reduction; Data Quality Program; Data Quality Lifecycle.
質問 # 43
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:
正解：E
解説：
A proliferation of independently implemented data warehouses, lakes, and poorly governed "data swamps" commonly indicates that the organization has emphasized technology acquisition over establishing an integrated, authoritative view of enterprise data. The issue is not the existence of multiple technologies by itself; distributed platforms can be justified by legitimate architectural requirements. The warning sign is uncontrolled duplication without clear authoritative sources, enterprise architecture, governance, lineage, and consistency controls.
DAMA-DMBOK2 treats Data Architecture as the mechanism for aligning data assets with enterprise strategy and reducing unnecessary fragmentation. A mature architecture identifies authoritative sources, data flows, integration patterns, shared definitions, and the required relationship between operational and analytical stores. The DAMA framework also emphasizes that technologies should support business and data- management objectives rather than dictate them.
From a Data Quality perspective, uncontrolled proliferation creates competing versions of values, inconsistent transformations, duplicate master records, contradictory metrics, and unclear ownership. These conditions undermine consistency and make root-cause analysis difficult.
A "single point of truth" does not necessarily require one physical database. It requires controlled authority and consistent semantics across the landscape.
Reference Topics: DAMA-DMBOK2 - Data Architecture; Data as an Asset; Authoritative Sources; Data Integration; Metadata; Data Quality Consistency.
質問 # 44
Database monitoring tools measure key database metrics, such as:
正解：E
解説：
DAMA-DMBOK2 identifies capacity, availability, cache performance, and user statistics as representative metrics captured by database-monitoring tools. Database monitoring is an operational control mechanism used by Database Administrators and platform teams to understand whether database infrastructure is available, adequately sized, responsive, and being used as expected. DMBOK2 explicitly describes monitoring tools as automating the observation of metrics such as capacity, availability, cache performance, and user statistics.
Capacity metrics indicate resource consumption and future growth requirements. Availability measures whether data services remain accessible when required. Cache-performance measures help identify inefficient access patterns and bottlenecks, while user statistics provide information about workload and database consumption.
The other choices mix design concepts, CRUD operations, or Data Quality concepts with operational monitoring measures. For example, normalization is a modelling technique rather than a routine runtime performance metric. Similarly, "create, read, update, delete" describes basic data operations rather than monitoring indicators.
Although database performance and Data Quality are distinct disciplines, poor operational performance can affect Data Quality dimensions such as timeliness and availability. Monitoring therefore supports the technical environment in which governed, reliable information is delivered to users and applications.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Database Operations; Capacity and Availability Management; Monitoring; Chapter 13 - Timeliness and Operational Fitness for Purpose.
質問 # 45
Obfuscation or redaction of data is the practice of:
正解：C
解説：
DAMA-DMBOK2 defines obfuscation or redaction as making information anonymous or removing sensitive information. The purpose is to reduce the risk that protected, confidential, or personally identifiable information can be exposed to users or processes that do not require access to the original values.
Obfuscation can involve masking, substitution, shuffling, temporal variation, partial display, or other methods that change what the recipient sees while preserving sufficient utility for the intended activity. For example, a customer-service agent may see only the final digits of an account identifier, or a development team may receive realistic but anonymized production-derived test data.
Redaction may remove information entirely from a representation when there is no legitimate requirement to expose it.
The technique is therefore a Data Security and privacy control, not database compression, publication, or classification.
There is also an important Data Quality consideration. Masked or obfuscated data used for testing must remain structurally valid and retain required relationships so that applications behave realistically. Metadata should indicate that a dataset has been transformed for privacy purposes so consumers do not mistake masked values for authoritative production data.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Obfuscation; Redaction; Data Masking; Privacy; Sensitive Information; Chapter 13 - Fitness for Purpose and Controlled Transformation.
質問 # 46
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