DAMA인증 DQ-1220시험은 중요한 IT인증자격증을 취득하는 필수시험과목입니다DAMA인증 DQ-1220시험을 통과해야만 자격증 취득이 가능합니다.자격증을 많이 취득하면 자신의 경쟁율을 높여 다른능력자에 의해 대체되는 일은 면할수 있습니다.Fast2test에서는DAMA 인증DQ-1220시험대비덤프를 출시하여 여러분이 IT업계에서 더 높은 자리에 오르도록 도움드립니다. 편한 덤프공부로 멋진 IT전문가의 꿈을 이루세요.
| Section | Weight | Objectives |
|---|---|---|
| Data Quality Assessment and Measurement | 25% | - Profiling Techniques - Root Cause Analysis - Metrics and KPIs - Benchmarking |
| Data Quality Frameworks and Standards | 20% | - Other Industry Standards - DAMA DMBOK Framework - ISO 8000 (Data Quality) |
| Data Quality Governance | 15% | - Data Stewardship - Continuous Monitoring - Data Quality Roles and Responsibilities - Policies and Procedures |
| Data Quality Improvement | 20% | - Data Cleansing Techniques - Standardization - Validation Rules - Data Enrichment |
| Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality |
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질문 # 87
The Data Architecture workstream on results produces:
정답：A
설명：
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.
질문 # 88
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
정답：C
설명：
The organization should determine the root cause and authoritative remediation point before multiple teams independently modify downstream copies.
If a defective value originates in a source system and is distributed to five downstream applications, correcting those five copies independently may provide temporary relief but leaves the upstream defect intact.
The incorrect value may simply be redistributed again.
Lineage should be used to trace the defect through source systems, transformations, integration layers, Master Data hubs, warehouses, and reports. Once the origin is understood, governance can determine which system or process is authoritative and where correction should occur.
This approach minimizes inconsistent local fixes and reduces the risk that different teams apply incompatible interpretations of the same issue.
DAMA's Chapter 13 revision explicitly strengthens the relationship between Data Quality and Metadata Management, Reference/Master Data Management, Modeling, and Data Integration. Those connections are precisely what enable cross-system issue resolution.
Downstream correction may still be required after the authoritative source is fixed, but it should be coordinated.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Issue Management; Metadata Lineage; Authoritative Sources; Data Integration.
질문 # 89
A company has five systems that can update a customer's legal name, and no system is designated as authoritative. What is the greatest Data Quality risk?
정답：C
설명：
The principal risk is inconsistent customer-name values across systems. When multiple applications can independently update the same business fact without an authoritative source or coordinated Master Data process, conflicting representations are likely to emerge.
The problem is architectural and governance-related as much as it is a Data Quality issue. The organization must determine where the authoritative value is created, who is responsible for approving changes, how updates propagate, and how conflicts are resolved.
DAMA's public framework emphasizes a single trusted or consistent approach to shared core entities through Reference and Master Data Management.
The relevant Data Quality dimensions include Consistency, Accuracy, Currency, and potentially Uniqueness.
An application may hold a technically valid legal name but still contain an outdated or conflicting value.
Metadata and lineage should document which systems create, master, distribute, or merely consume the attribute.
Reference Topics: DAMA-DMBOK2 - Authoritative Sources; Master Data Management; Data Architecture; Consistency; Lineage; Stewardship.
질문 # 90
What is a steward?
정답：D
설명：
In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: "A steward is a person whose job it is to manage the property of another person." It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.
A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.
The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.
A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 - Data Quality Governance and Responsibilities.
질문 # 91
A Data Quality team has identified 500 defects across several domains. Which factor should have the strongest influence on remediation priority?
정답：A
설명：
Remediation should principally be prioritized according to business impact and risk. Not all defects have equivalent consequences, even when they occur at similar frequencies.
A defect affecting regulatory reporting, customer payments, safety-critical operations, executive reporting, or high-value master data may require immediate remediation. A larger number of defects affecting a low- impact optional field may legitimately receive lower priority.
A robust prioritization model may consider financial loss, regulatory exposure, operational disruption, customer impact, reputational damage, number of dependent systems, recurrence rate, remediation cost, and whether a Critical Data Element is involved.
This risk-based approach prevents Data Quality programs from becoming simple defect-count reduction exercises. The objective is not merely to maximize the number of corrected records but to improve fitness for purpose where poor data creates material consequences.
Governance should approve prioritization criteria and resolve conflicts where different business areas assign different importance to the same issue. Metadata and lineage provide evidence about downstream dependencies and affected processes.
DAMA's revision of Chapter 13 adds a clearer Critical Data Element concept and clarifies responsibility within the Data Quality Improvement Lifecycle, reinforcing risk-based prioritization.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Issue Prioritization; Business Impact; Critical Data Elements; Risk; Remediation.
질문 # 92
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