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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Data Stewardship - Policies and Procedures - Continuous Monitoring |
| Topic 2: Data Quality Frameworks and Standards | 20% | - Other Industry Standards - ISO 8000 (Data Quality) - DAMA DMBOK Framework |
| Topic 3: Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Topic 4: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Benchmarking - Profiling Techniques - Metrics and KPIs |
| Topic 5: Data Quality Improvement | 20% | - Standardization - Validation Rules - Data Enrichment - Data Cleansing Techniques |
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NEW QUESTION # 12
A Data Quality team has identified 500 defects across several domains. Which factor should have the strongest influence on remediation priority?
Answer: A
Explanation:
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.
NEW QUESTION # 13
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
Answer: C
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 # 14
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 # 15
The implementation of data architecture exposes the transformation of data as it moves across the landscape.
A common name for this concept is:
Answer: B
Explanation:
The concept described is Data Lineage. Data lineage records how data originates, moves, transforms, and is consumed across the information landscape. DAMA-oriented architecture guidance explicitly links implementation of Data Architecture with visibility into transformations occurring as data traverses systems and identifies this as lineage.
Lineage can operate at several levels. At a high level, it may identify that customer information moves from CRM into an integration platform, Master Data hub, warehouse, and reporting environment. At a detailed level, it may show that a particular report column derives from a specific source attribute through documented calculations, mappings, and transformation rules.
This capability is fundamental to Data Quality. When an incorrect result appears in a report, lineage helps analysts trace the defect upstream to the point where it originated or was introduced. It also supports root- cause analysis, impact assessment, regulatory traceability, change management, and reconciliation.
Metadata Management provides the repository structures needed to capture and maintain lineage. Data Governance determines which lineage must be documented and who is accountable for maintaining it.
ETL is one mechanism through which data may move and transform, but ETL is not the architectural concept describing the end-to-end history and derivation of the data.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Chapter 11 - Metadata Management; Data Lineage; Chapter 13 - Root-Cause Analysis and Data Quality Traceability.
NEW QUESTION # 16
A Data Quality rule states that every active supplier must have a valid tax identifier. Who should approve whether the rule is mandatory for the Supplier domain?
Answer: C
Explanation:
Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.
A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.
The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.
This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.
DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance and Stewardship; Chapter 13 - Data Quality Rules; Metadata Management; Supplier Master Data.
NEW QUESTION # 17
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