DAMA DQ-1220 Dumps Questions Answers

When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.

DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.

Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.

Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.

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A Business Continuity Plan is fundamentally concerned with maintaining essential business operations when normal services are disrupted. Therefore, the correct response is that it outlines how the business will continue operating during an unplanned disruption.

Within the DAMA-DMBOK2 framework, continuity concerns interact particularly with Data Governance, Data Storage and Operations, Data Security, and operational metadata. The DMBOK2 index explicitly identifies Business Continuity Plans and associates them with operational resilience and recovery concepts. A continuity plan must address how critical processes, information assets, personnel, technology, dependencies, and recovery arrangements will sustain or restore required business capability. The established definition of a BCP likewise centers on continued operation during unplanned service disruption.

Option E is incomplete: identifying potential disruptions belongs to risk assessment and continuity planning, but merely listing disruptions does not explain how operations will continue. Options B and D concern communications rather than continuity itself.

From a Data Quality perspective, continuity controls also protect availability, timeliness, integrity, and reliability. Recovery arrangements must ensure that restored data is complete, current enough for business use, and reconciled against authoritative sources after disruption.

Reference Topics: DAMA-DMBOK2 — Data Governance; Data Storage and Operations; Business Continuity; Disaster Recovery; Backup and Recovery; Chapter 13 — Timeliness, Integrity and Fitness for Purpose.

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DAMA-DMBOK2 assigns primary responsibility for business glossary content to Business Data Stewards. Business Data Stewards are typically subject-matter experts who understand how data is defined, created, interpreted, and consumed within their business domain. DMBOK2 explicitly states that Data Stewards are generally responsible for business glossary content and identifies Business Data Stewards as professionals who work with stakeholders to define and control data.

A business glossary is not simply a technical dictionary. It establishes agreed business terminology, definitions, synonyms, business rules, responsible stewards, and relationships between business concepts. This makes stewardship involvement essential because definitions must reflect operational and business meaning rather than merely database structures.

Data Architects may contribute candidate definitions and structural context from subject-area and conceptual models, but they do not generally own the business meaning. Business users provide valuable input, while Coordinating Data Stewards help reconcile definitions across domains, yet the normal accountability remains with Business Data Stewards.

This relationship is particularly important for Data Quality. Quality rules depend on unambiguous definitions of data elements. If “Customer,” “Active Account,” or “Order Date” has inconsistent meanings, measurements of completeness, accuracy, and validity cannot be consistently interpreted.

Reference Topics: DAMA-DMBOK2 Chapter 3 — Develop a Business Glossary; Business Data Stewardship; Metadata Management; Chapter 13 — Data Quality Rules and Business Definitions.

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