私たちの世界は絶え間ない変化と進化の状態にあります。時間のペースを保ち、絶えず変化し、自分自身に挑戦したい場合は、1種類のDQ-1220証明書テストに参加して、実用的な能力を向上させ、知識の量を増やしてください。 DQ-1220学習実践ガイドを購入すると、テストにスムーズに合格できます。 DQ-1220試験資料は、上級専門家による厳密な分析と検証を経ており、いつでも新しいリソースを補足する準備ができています。
DAMA DQ-1220（データ品質）認定試験は、データ品質管理の分野で働く個人の知識とスキルを検証する専門認定試験です。この認定試験は、組織のデータの正確性、完全性、一貫性を保証する責任を持つデータ専門家を対象に設計されています。試験は、データプロファイリング、データクレンジング、データガバナンス、データ品質測定など、データ品質管理に関連する広範囲のトピックをカバーしています。
この認定は、情報とデータ管理の理解と実践を促進する非営利組織であるDama Internationalから授与されます。 DAMA Internationalは、データ管理の専門家の主要な組織として世界中で認められており、その認定は業界で非常に尊敬されています。
世界大手の企業の中で、大部分の企業はDAMA製品を主として運用しています。だから、DAMAの認証を取得したら、激しい競争の中でもいい仕事を探せます。受験生は試験に合格したいなら、DQ-1220問題集をしようするのは一番迅速の方法です。多くの受験生たちはこの方法を通して試験に合格しました。
試験の準備には、候補者はDAMA Internationalまたはその他のトレーニングプロバイダーが提供するトレーニングコースに参加することができます。トレーニングは、データ品質に関連する重要な概念やベストプラクティスをカバーし、候補者に試験に合格するために必要な知識を提供します。さらに、候補者は、書籍、オンラインリソース、模擬試験などの学習教材を使用して、試験でカバーされるトピックの理解を強化することができます。
質問 # 72
The Data Architecture workstream on results produces:
正解：B
解説：
Within DAMA-DMBOK2, establishing a Data Architecture practice is divided into several complementary workstreams. The Results workstream is specifically responsible for producing Data Architecture artifacts within an overall roadmap. The other answer choices correspond to different aspects of the architecture practice rather than to the Results workstream itself.
DMBOK2 distinguishes the workstreams clearly: Strategy selects frameworks and develops the roadmap; Acceptance and Culture addresses awareness and behavioural change; Organization assigns architecture accountability and responsibility; Working Methods establish practices and coordinate architecture work with development initiatives; and Results produces the architectural artifacts that collectively realize the roadmap.
Typical artifacts include enterprise data models, high-level data-flow designs, architectural standards, transition-state specifications, and related metadata. These artifacts represent both current and target architectural states and allow individual projects to align with enterprise direction.
The Data Quality connection is substantial because architectural artifacts identify authoritative sources, integration points, critical data flows, entities, relationships, and dependencies. Those structures provide the context needed to establish validity, integrity, consistency, lineage, and quality-monitoring requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Establish Data Architecture Practice; Results Workstream; Data Architecture Artifacts; Architecture Roadmap; Chapter 13 - Data Quality Dependencies.
質問 # 73
The goals of data storage and operations are:
正解：A
解説：
DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.
Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.
Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.
Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.
Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.
The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 - Data Reliability and Timeliness.
質問 # 74
HTTPS:// indicates that the website is:
正解：C
解説：
An address beginning with HTTPS indicates that the website uses an encrypted security layer for communications between the client and server. DAMA-DMBOK2 explicitly identifies HTTPS as a security technology and states that the https:// prefix indicates that a website is equipped with an encrypted security layer.
HTTPS uses TLS to protect information while it is transmitted across the network. Its principal security objectives include confidentiality of the communication channel, integrity of transmitted information, and authentication of the server through digital certificates. This is especially important when transmitting credentials, personal information, payment details, or other sensitive data.
HTTPS does not indicate what database technology the website uses, whether a content management system is installed, whether third-party cookies are present, or whether language-translation functionality exists.
Those characteristics are independent of transport-layer encryption.
From a Data Management standpoint, encryption in transit is one component of a broader Data Security framework. Sensitive data must also be protected at rest, appropriately classified, access-controlled, monitored, and governed throughout its lifecycle.
Data Quality also benefits indirectly because integrity protection helps prevent unauthorized alteration of values during transmission.
Reference Topics: DAMA-DMBOK2 Chapter 7 - HTTPS; Encryption; Data in Transit; Authentication; Confidentiality; Integrity.
質問 # 75
The seven Vs of Big Data are:
正解：B
解説：
Within the DMBOK2 framing used by this certification material, the expanded characteristics are Volume, Velocity, Variety/Variability, Viscosity, Volatility, and Veracity. Option E is therefore the only choice that correctly contains the DAMA terms represented in the question. DMBOK-oriented study material explains that the original three Vs-Volume, Velocity, and Variety-were expanded to include Variability, Viscosity, Volatility, and Veracity in the broader characterization.
Volume concerns data quantity; Velocity concerns the rate of creation and processing; Variety/Variability concerns differing structures and changing representations; Viscosity describes difficulty in using or integrating the data; Volatility concerns how quickly usefulness or meaning changes; and Veracity addresses credibility and trustworthiness.
These characteristics have direct Data Quality implications. Increased variety creates semantic and structural consistency challenges. Velocity reduces the time available for traditional validation. Volatility affects currency and timeliness. Veracity is directly concerned with reliability.
DAMA's key point is that Big Data does not reduce the need for management discipline. Its scale and complexity increase the need for metadata, governance, automated profiling, lineage, and statistically driven quality controls.
Reference Topics: DAMA-DMBOK2 Big Data and Data Science - Big Data Characteristics; Volume; Velocity; Variety/Variability; Viscosity; Volatility; Veracity; Chapter 13 - Scalable Data Quality.
質問 # 76
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
正解：E
解説：
A complete absence of reported issues immediately after introducing an enterprise Data Issue process is more likely to indicate lack of confidence in the governance process than genuinely flawless enterprise data. The certification material explicitly identifies lack of credibility in the process's ability to effect change as the plausible explanation.
Effective issue management depends on organizational trust. Employees need to believe that documenting an issue will lead to triage, ownership, escalation, root-cause investigation, remediation, and appropriate communication. If previous problems disappeared into a queue without action-or if raising defects creates organizational friction-users may simply stop reporting them.
DAMA-DMBOK2 treats Data Governance implementation as an organizational-change challenge rather than a purely procedural exercise. Governance must demonstrate authority, responsiveness, transparency, and measurable outcomes to establish credibility.
For Data Quality, issue volumes must also be interpreted carefully. "Zero issues reported" is not equivalent to
"zero defects." Complementary evidence should come from profiling, automated monitoring, quality metrics, user feedback, reconciliation, and operational outcomes.
Management should investigate reporting barriers, communicate resolved cases, establish clear escalation paths, and demonstrate that identified problems produce tangible improvements.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Adoption and Credibility; Data Issue Management; Chapter 13 - Issue Identification, Escalation, Root-Cause Analysis and Remediation.
質問 # 77
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DQ-1220試験関連情報: https://www.mogiexam.com/DQ-1220-exam.html