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Achieving the DQ-1220 certification demonstrates a high level of competence in data quality management and can enhance the career prospects of professionals in this field. It also provides organizations with a benchmark for assessing the skills and knowledge of their employees in data quality management.
DAMA DQ-1220 exam is a certification program that tests an individual's knowledge and expertise in the field of data quality. DAMA International, the organization behind the certification, is a global nonprofit association that focuses on advancing the concepts and practices of information management. The DQ-1220 Exam is one of the many certifications they offer, and it specifically focuses on the principles, methods, and best practices for managing and ensuring the quality of data.
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The purpose of the DAMA DQ-1220 Certification Exam is to provide professionals with the knowledge and skills necessary to effectively manage and maintain data quality within their organizations. DQ-1220 exam covers a wide range of topics, including data quality concepts, data profiling techniques, data cleansing practices, and data governance principles. By passing the certification exam, professionals can demonstrate their expertise in these areas and increase their credibility as data quality experts.
NEW QUESTION # 74
What is the final step in the development of a business-data-driven roadmap?
Answer: A
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 75
The seven Vs of Big Data are:
Answer: E
Explanation:
Within the DMBOK2 framing used by this certification material, the expanded characteristics are Volume, Velocity, Variety/Variability, Viscosity, Volatility, and Veracity. Option E is therefore the only choice that correctly contains the DAMA terms represented in the question. DMBOK-oriented study material explains that the original three Vs-Volume, Velocity, and Variety-were expanded to include Variability, Viscosity, Volatility, and Veracity in the broader characterization.
Volume concerns data quantity; Velocity concerns the rate of creation and processing; Variety/Variability concerns differing structures and changing representations; Viscosity describes difficulty in using or integrating the data; Volatility concerns how quickly usefulness or meaning changes; and Veracity addresses credibility and trustworthiness.
These characteristics have direct Data Quality implications. Increased variety creates semantic and structural consistency challenges. Velocity reduces the time available for traditional validation. Volatility affects currency and timeliness. Veracity is directly concerned with reliability.
DAMA's key point is that Big Data does not reduce the need for management discipline. Its scale and complexity increase the need for metadata, governance, automated profiling, lineage, and statistically driven quality controls.
Reference Topics: DAMA-DMBOK2 Big Data and Data Science - Big Data Characteristics; Volume; Velocity; Variety/Variability; Viscosity; Volatility; Veracity; Chapter 13 - Scalable Data Quality.
NEW QUESTION # 76
Who does the DMBoK consider to be generally responsible for developing business glossary content?
Answer: A
Explanation:
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.
NEW QUESTION # 77
What is the purpose of Data Governance?
Answer: D
Explanation:
The precise DAMA-DMBOK2 purpose of Data Governance is to ensure that data is managed properly according to policies and best practices. DMBOK2 distinguishes this governance purpose from the broader objective of Data Management, which is concerned with obtaining value from data throughout its lifecycle.
Governance establishes the authority and control framework within which operational data-management functions work. This includes strategy, policy, standards, accountability, stewardship, compliance, issue escalation, quality oversight, and decision rights. DMBOK2 specifically identifies governance responsibilities involving policies for metadata, access, security and quality; setting Data Quality and Data Architecture standards; and providing oversight and corrective action.
This distinction is critical for Data Quality. Data Quality Management performs activities such as profiling, measurement, root-cause analysis, cleansing and monitoring. Data Governance determines who has authority, which requirements are mandatory, what thresholds are acceptable, which issues require escalation, and who owns remediation decisions.
Metadata Management supports governance by recording authoritative definitions, lineage, ownership and quality rules. Master Data Management operationalizes governance for shared business entities and reference domains.
Options B, C and D describe legitimate aspects of wider Data Management, but they do not state the specific purpose assigned to Data Governance by DMBOK2.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Introduction; Goals and Principles; Governance Oversight; Chapter 13 - Data Quality and Governance.
NEW QUESTION # 78
Who should normally define the business meaning and acceptable quality requirements for a critical customer attribute?
Answer: D
Explanation:
A Business Data Steward, working with relevant business stakeholders and governance bodies, is the appropriate role to define or coordinate business meaning and quality expectations.
Quality requirements cannot be derived solely from database structures. The organization must understand what the attribute means, how it is used, what values are acceptable, which source is authoritative, and what consequences arise when it is wrong.
Data Stewards bridge business knowledge and formal Data Management controls. They commonly participate in maintaining glossary definitions, defining business rules, resolving issues, clarifying ownership, and establishing Data Quality expectations.
Technical specialists contribute implementation knowledge. A DBA may identify datatype constraints, while an integration specialist can implement transformations. Neither should independently determine business meaning.
DAMA's public framework describes Metadata Management as supporting definitions, lineage, and governance, while Reference and Master Data Management ensures consistency in shared core entities.
The strongest operating model therefore combines business accountability with technical execution.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Chapter 11 - Business Metadata; Chapter 13 - Data Quality Requirements; Critical Data Elements.
NEW QUESTION # 79
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