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DAMA DQ-1220 시험은 데이터 품질 분야의 경력을 추구하는 데 관심이있는 개인뿐만 아니라 이미 현장에서 일하고 있으며 자신의 숙련도를 보여 주려고하는 사람들을위한 것입니다. 시험은 또한 데이터 품질 인력의 기술을 평가하고 개선을위한 영역을 식별하려는 조직에도 유용합니다.
DAMA DQ-1220 자격증 시험은 데이터 품질 평가, 데이터 품질 측정, 데이터 거버넌스, 데이터 프로파일링 및 데이터 클렌징을 포함한 다양한 주제를 다룹니다. 이 시험은 객관식 시험과 케이스 스터디로 나뉘어져 있으며, 두 부분 모두 후보자의 실제 시나리오에서 지식과 기술을 적용할 수 있는 능력을 테스트하기 위해 설계되었습니다. 시험은 도전적이며, 후보자는 데이터 품질 개념에 대한 견고한 이해력이 필요합니다.
꿈을 안고 사는 인생이 멋진 인생입니다. 고객님의 최근의 꿈은 승진이나 연봉인상이 아닐가 싶습니다. DAMA인증 DQ-1220시험은 IT인증시험중 가장 인기있는 국제승인 자격증을 취득하는데서의 필수시험과목입니다.그만큼 시험문제가 어려워 시험도전할 용기가 없다구요? 이제 이런 걱정은 버리셔도 됩니다. ITDumpsKR의 DAMA인증 DQ-1220덤프는DAMA인증 DQ-1220시험에 대비한 공부자료로서 시험적중율 100%입니다.
시험은 데이터 거버넌스, 데이터 프로파일 링, 데이터 정리, 데이터 모니터링 및 데이터 감사를 포함한 데이터 품질 관리의 다양한 측면을 다룹니다. 시험은 또한 개인의 데이터 품질 라이프 사이클에 대한 이해와 다양한 데이터 품질 문제를 해결하는 방법을 테스트합니다. DAMA DQ-1220 시험은 데이터 분석가, 데이터 품질 분석가, 데이터 청지기, 데이터 아키텍트, 데이터 과학자 및 데이터를 사용하는 기타 전문가에게 이상적입니다.
질문 # 58
Three common interaction models for data integration are:
정답：B
설명：
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
질문 # 59
The goals of data storage and operations are:
정답：B
설명：
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.
질문 # 60
A customer moved house three months ago. The CRM still contains the customer's former address, although the record was originally correct when created. Which Data Quality dimension best describes the current problem?
정답：A
설명：
The issue is Currency. Currency concerns whether data remains sufficiently aligned with the current real- world state. The address was accurate when originally captured, but reality changed and the system was not updated.
This distinction matters because Data Quality can degrade over time without any technical error being introduced. Customer addresses, employment details, product prices, organizational structures, regulatory classifications, and contact details all have varying rates of change. A dataset that was reliable last year may no longer be fit for operational use today.
DAMA's current Chapter 13 revision explicitly identifies Currency as one of the nine standard Data Quality dimensions. Related DAMA-derived guidance also notes that real-world information changes and can therefore become outdated even when it was initially correct.
Organizations should establish refresh expectations according to business use. A marketing mailing list may tolerate a different update interval from an emergency-contact database.
Metadata should document refresh frequency and source authority, while quality monitoring can identify records exceeding acceptable age thresholds.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Currency; Accuracy; Timeliness; Data Aging; Monitoring; Fitness for Purpose.
질문 # 61
A customer database contains postal addresses but lacks latitude and longitude. The organization adds geographic coordinates obtained from a trusted external service. This activity is an example of:
정답：D
설명：
This is Data Enrichment. Enrichment enhances existing records by adding useful information derived from internal or external sources. The original postal address remains intact, while geographic coordinates are appended to make the customer record more useful for location analysis, logistics, service coverage, route planning, or segmentation.
Enrichment differs from cleansing. Cleansing corrects or standardizes defective data; enrichment adds information that was not previously present. A process may perform both-for example, standardizing an address before passing it to a geocoding service.
External enrichment introduces governance and quality considerations. The organization should assess source reliability, licensing and permitted usage, refresh frequency, match confidence, geographical precision, lineage, and whether enrichment values should be treated as authoritative or derived.
Metadata should record that latitude and longitude were obtained from an external geocoding process rather than directly supplied by the customer. Quality metrics may measure match rate, confidence, completeness, or positional accuracy.
DAMA identifies Data Quality Management as including error detection, cleansing, and enrichment strategies, while Metadata Management supports understanding of lineage, definitions, and usage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Enrichment; Cleansing; Profiling; Metadata Lineage; External Data Sources.
질문 # 62
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:
정답：C
설명：
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.
질문 # 63
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