Itcertkr의 경험이 풍부한 IT전문가들이 연구제작해낸 DAMA인증 DQ-1220덤프는 시험패스율이 100%에 가까워 시험의 첫번째 도전에서 한방에 시험패스하도록 도와드립니다. DAMA인증 DQ-1220덤프는DAMA인증 DQ-1220최신 실제시험문제의 모든 시험문제를 커버하고 있어 덤프에 있는 내용만 공부하시면 아무런 걱정없이 시험에 도전할수 있습니다.
DAMA DQ-1220 시험은 선택형 지식 시험과 실제 시나리오에서 이러한 개념을 적용할 수 있는 능력을 시험하는 실전 시험으로 나뉩니다. 지식 시험은 데이터 품질 관리 개념과 실천 방법에 대한 지원자의 이해도를 평가하며, 실전 시험은 이러한 개념을 실제 상황에서 적용할 수 있는 능력을 시험합니다. 시험은 온라인 및 종이 기반 형식으로 제공되며, 지원자는 연중 언제든지 시험을 볼 수 있습니다.
시험을 준비하기 위해서는, 응시자는 시험 목표와 DAMA International이 제공하는 학습 자료를 검토해야 합니다. 이러한 자료는 시험에서 출제될 모든 주제를 다루며, 주요 개념과 원칙에 대한 깊은 설명을 제공합니다.
DAMA인증DQ-1220시험준비를 하고 계시다면Itcertkr에서 출시한DAMA인증DQ-1220덤프를 제일 먼저 추천해드리고 싶습니다. Itcertkr제품은 여러분들이 제일 간편한 방법으로 시험에서 고득점을 받을수 있도록 도와드리는 시험동반자입니다. DAMA인증DQ-1220시험패는Itcertkr제품으로 고고고!
DAMA DQ-1220 인증 시험은 전 세계적으로 인정되며 업계에서 높이 평가됩니다. 인증은 개인이 고품질 데이터를 관리하고 유지하는 데 필요한 지식과 기술을 가지고 있음을 보여줍니다. 비즈니스 세계에서 데이터의 중요성이 높아짐에 따라이 인증은 매일 데이터를 사용하는 개인에게는 점점 더 가치가 높아지고 있습니다.
질문 # 107
A Data Quality score is calculated from Completeness, Accuracy, and Timeliness. Why might the dimensions be weighted differently?
정답：D
설명：
Dimensions may be weighted differently because their business impact is not necessarily equal for a particular dataset or process.
For an emergency-response system, Timeliness may be more critical than completeness of optional descriptive fields. For regulatory reporting, Accuracy and Integrity may carry substantially greater risk than minor delays. For a marketing contact list, completeness and currency may dominate.
A composite Data Quality score should therefore reflect business priorities rather than assuming that each dimension contributes identical value. Weighting must be transparent and governed; otherwise a high score in low-impact dimensions could hide serious failure in a critical one.
The first step remains defining business requirements and Critical Data Elements. Once those are established, dimensions, thresholds, and weights can be linked to documented business consequences.
A score should also retain underlying component metrics. A single aggregate percentage is insufficient for diagnosis because two datasets with the same overall score may have very different risk profiles.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Metrics; Scorecards; Business Impact; Critical Data Elements; Quality Dimensions; Thresholds.
질문 # 108
What are the types of models that are modelled in data models?
정답：D
설명：
DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.
Category information classifies things-for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.
Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.
This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.
Data models also generate important metadata-definitions, relationships, domains and constraints-which becomes the basis for measurable Data Quality rules.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Types of Data that are Modeled; Chapter 10 - Reference and Master Data; Chapter 13 - Data Quality Rules and Dimensions; Metadata Management.
질문 # 109
Before defining a Data Quality metric for a business-critical field, the team should first:
정답：D
설명：
A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.
For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.
The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.
DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.
Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.
Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.
질문 # 110
A goal of reference and master data management is for data to ensure shared data is:
정답：C
설명：
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
질문 # 111
A pensioner who usually receives a quarterly bill of around $300 was sent a $100,000,000 electricity bill.
They were a victim of poor data quality checks in which dimension?
정답：C
설명：
The failed control is principally Reasonableness. DAMA treats reasonableness as the extent to which data values or patterns conform to credible expectations. The current DMBOK2 revision recognizes Reasonableness among the standard Data Quality dimensions, alongside Accuracy, Validity, Completeness, Integrity, Uniqueness, Timeliness, Currency and Consistency.
A $100,000,000 residential electricity bill may be syntactically valid: the field may accept the number, the record may satisfy referential constraints, and the value may have arrived on time. Nevertheless, it is grossly inconsistent with the customer's historical pattern of approximately $300 per quarter. A reasonableness control would therefore compare the amount with expected ranges, historical consumption, statistical limits, peer-group distributions, or business thresholds and flag the value before billing.
This illustrates an important distinction from Accuracy. Accuracy asks whether the stored value correctly represents reality. Reasonableness asks whether the value is credible in context. The absurd magnitude itself is detectable without first obtaining an independently verified "true" bill value.
Practical controls include range checks, deviation-from-history rules, z-score/outlier detection, control limits, and exception thresholds. Such rules should be governed as metadata and owned by an appropriate Data Steward.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Dimensions; Reasonableness; Measurement and Monitoring; Statistical Process Control; Data Quality Rules.
질문 # 112
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