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To be eligible to take the DQ-1220 certification exam, candidates must have a minimum of two years of experience in the field of data management or related areas. Candidates are also required to have a high school diploma or equivalent education. DQ-1220 Exam consists of 100 multiple choice questions and candidates are given 2 hours to complete it. DQ-1220 exam is available in English and is administered at authorized testing centers or via online proctoring.
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NEW QUESTION # 12
Before defining a Data Quality metric for a business-critical field, the team should first:
Answer: D
Explanation:
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.
NEW QUESTION # 13
The DAMA Wheel contains:
Answer: E
Explanation:
The DAMA Wheel represents the principal Data Management Knowledge Areas defined by DAMA- DMBOK2. Data Governance occupies the central position because governance provides authority, coordination, standards, and oversight across the other disciplines. The surrounding areas include Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, and Data Quality. DAMA descriptions of the framework identify these as Knowledge Areas rather than project deliverables or maturity dimensions.
The Wheel is deliberately integrative. No Data Management discipline operates effectively in complete isolation. For example, Data Quality relies on Governance for authority and accountability, Metadata Management for definitions and lineage, Reference and Master Data for consistent shared entities and codes, Data Architecture for structural context, and Integration for controlled movement between systems.
This interaction is central to Chapter 13. Data Quality cannot be reduced to cleansing defective records after problems appear. Sustainable quality requires coordinated management throughout the lifecycle and across the other DAMA knowledge areas.
Data management processes, deliverables, strategic initiatives, and maturity assessments are all legitimate DAMA concepts, but they are not what the DAMA Wheel itself principally represents.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; DAMA Wheel; Knowledge Areas; Chapter 3 - Governance; Chapter 13 - Data Quality Relationships with Other Knowledge Areas.
NEW QUESTION # 14
Which of the following activities is NOT a way that enterprise data architecture influences the scope boundaries of projects?
Answer: A
Explanation:
Ensuring enterprise business processes are effectively documented is not primarily a Data Architecture mechanism for defining project scope boundaries. That responsibility belongs more directly to Business Architecture, process management, and business-analysis disciplines.
DAMA-DMBOK2 explains that Enterprise Data Architecture influences projects by defining enterprise data requirements, reviewing project data designs, determining lineage impacts, controlling unnecessary replication, enforcing Data Architecture standards, and guiding technology and renewal decisions.
Consequently, options A, B, D, and E all represent valid ways architecture can constrain or guide project scope. A design review checks that local solutions remain compatible with enterprise strategy. Standards prevent individual projects from creating incompatible structures. Replication controls limit uncontrolled proliferation of redundant data. Enterprise data requirements ensure that projects account for information needs beyond their immediate application boundaries.
Documenting business processes remains important because processes produce and consume data, but comprehensive process documentation is not itself a primary Enterprise Data Architecture scope-control activity.
From a Data Quality perspective, architectural influence prevents project-level decisions from creating duplicated authoritative sources, inconsistent definitions, undocumented lineage, or uncontrolled transformations.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Manage Enterprise Requirements within Projects; Architecture Governance; Project Scope Boundaries; Data Replication; Chapter 13 - Consistency and Lineage.
NEW QUESTION # 15
What is one of the Data Architecture artifacts which are usually captured within development projects, and then standardised and managed by data architects?
Answer: A
Explanation:
The correct answer is Data models. DAMA-DMBOK2 explicitly explains that data models and other Data Architecture artifacts are commonly created or captured within development projects and are subsequently standardized and managed by Data Architects.
Development projects routinely produce conceptual, logical, and physical representations of the data required by a solution. If each project develops those structures independently without architectural review, inconsistent terminology, duplicated entities, conflicting definitions, and incompatible relationship structures can accumulate across the enterprise. Data Architects therefore review project models, reconcile them with enterprise standards, and identify structures suitable for reuse.
An Enterprise Data Model is itself an important architectural artifact, but the wording of the question refers specifically to artifacts typically generated in individual development projects and later standardized. The DMBOK2 passage uses data models in precisely that context.
This process has a direct Data Quality benefit. Standardized models improve consistency of definitions, domains, keys, relationships, and constraints. They also provide metadata from which integrity, uniqueness, validity, and completeness rules can be derived.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Architectural Artifacts; Development Projects; Chapter 5 - Data Modeling and Design; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 16
A Data Quality team finds that 78% of customer complaints are caused by four defect categories out of twenty recorded categories. Which tool is most appropriate for prioritizing improvement activity?
Answer: C
Explanation:
Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.
The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.
The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship.
Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.
Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.
Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.
NEW QUESTION # 17
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