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DAMA DQ-1220 (Data Quality) certification exam is a popular certification among professionals who work with data. Data Quality certification exam is designed to test a candidate's knowledge and skills related to data quality, which is an essential aspect of data management. Data Quality certification is offered by the Data Management Association (DAMA), which is a global organization that provides education, certification, and networking opportunities to data management professionals.
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DAMA DQ-1220 Exam covers a wide range of topics related to data quality, including data profiling, data cleansing, data integration, data governance, and data stewardship. It also evaluates the candidate's understanding of data quality frameworks, data quality metrics, and data quality assessment methodologies. Additionally, the exam assesses the candidate's ability to apply data quality concepts to real-world scenarios and to make data quality recommendations.
Earning the DAMA DQ-1220 Certification can provide many benefits to individuals and organizations. It demonstrates a high level of knowledge and expertise in data quality management, which can lead to career advancement and increased job opportunities. For organizations, having employees with this certification can improve the quality of their data and lead to better decision-making, ultimately contributing to the overall success of the business.
NEW QUESTION # 42
A goal of reference and master data management is for data to ensure shared data is:
Answer: B
Explanation:
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
NEW QUESTION # 43
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
Answer: B
Explanation:
The correct definition is the fundamental organisation of a system, and the principles governing its design and evolution. DAMA-DMBOK2 incorporates the ISO/IEC 42010 architectural concept when establishing the theoretical basis for Data Architecture.
The fuller ISO formulation describes architecture in terms of the fundamental organization of a system embodied in its components, their relationships to one another and to the environment, together with the principles governing design and evolution. DAMA uses this concept to distinguish true architecture from a collection of diagrams or implementation specifications.
Option E is therefore incomplete: architectural artifacts document architecture, but the artifacts themselves are not the architecture. Option C focuses too narrowly on implementation and maintenance, while options B and D describe potential objectives or constraints rather than the definition.
Applied to enterprise data, this means architecture establishes the high-level organization of data assets, information flows, platforms, relationships, and guiding principles by which the data environment evolves.
Data Quality benefits because architectural principles establish where authoritative information originates, how it moves, how duplication is controlled, and where governance and quality controls must operate.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Architecture Definition; ISO/IEC 42010; Enterprise Data Architecture; Architectural Principles; Chapter 13 - Quality by Design.
NEW QUESTION # 44
An MDM hub receives three customer records that have been confirmed to represent the same person. Before creating the mastered representation, what process determines which source values should populate each attribute?
Answer: C
Explanation:
The process is Survivorship. After records have been matched and determined to represent the same entity, survivorship rules determine which attribute values should populate the mastered or preferred representation.
For example, the organization may select the most recently verified telephone number, prioritize a government-verified legal name, or prefer addresses originating from a designated Customer Relationship Management system. Different attributes can use different survivorship rules.
Survivorship is not simply "choose the newest value." The appropriate method may consider source trust, verification status, recency, completeness, confidence scores, stewardship decisions, or combinations of these factors.
The rules must be governed because they determine what the organization subsequently treats as trusted master data. Metadata should record the rule, selected source, lineage, and effective date so the mastered record remains explainable.
DAMA identifies Reference and Master Data Management as responsible for maintaining consistency in core entities such as customers, products, and locations and for supporting trusted shared information.
The Data Quality dimensions involved commonly include Accuracy, Consistency, Currency, Completeness, and Uniqueness.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Golden Record; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 45
What is the purpose of Data Governance?
Answer: A
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 # 46
Which of the following is the best example of a 'documented data quality rule'?
Answer: A
Explanation:
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
NEW QUESTION # 47
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