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| Section | Objectives |
|---|---|
| Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
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NEW QUESTION # 101
In data security, which of the following is not one of the four A's:
Answer: A
Explanation:
Agile is not one of the four A's of Data Security. DAMA security guidance groups core security processes around Access, Audit, Authentication, and Authorization. These capabilities collectively control who can interact with information resources, establish identity, determine permitted actions, and provide evidence of activity for monitoring and accountability. DAMA-aligned security references identify these four concepts directly.
Authentication confirms the identity of a user or system. Authorization determines what an authenticated identity is permitted to do. Access concerns the actual ability to obtain or interact with data and services.
Audit provides traceability by recording and reviewing relevant activities.
Agile, by contrast, is an approach to iterative delivery and project or product development. It may influence how security controls are implemented within development lifecycles, but it is not itself one of the foundational security-control categories identified in this model.
Data Security and Data Quality are separate but related disciplines. Unauthorized modifications can compromise accuracy and integrity, while weak auditability can prevent organizations from identifying how data was altered. Appropriate security controls therefore help preserve the reliability of governed information.
Metadata classification also supports these controls by identifying sensitive data and linking it to access and protection requirements.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Access; Authentication; Authorization; Audit; Data Governance; Chapter 13 - Integrity and Controlled Data Modification.
NEW QUESTION # 102
Data modelling tools and model repositories are necessary for:
Answer: E
Explanation:
DAMA-DMBOK2 states that Data Modeling tools and model repositories are necessary for managing the Enterprise Data Model at all levels. This includes maintaining relationships among conceptual, logical, and physical representations and controlling how models created for different purposes fit into the wider enterprise architecture.
An enterprise modeling repository provides more than diagramming. It supports version control, definitions, lineage between model layers, comparison of changes, reuse of standard entities and attributes, enforcement of modelling conventions, and communication across projects. Many modelling tools also provide relationship and lineage capabilities that enable architects to trace how concepts represented at a high level are implemented in detailed models.
Option C describes one important capability-visualizing and communicating database designs-but it is narrower than the DMBOK2 purpose stated in the question. Similarly, modelling tools support governance and architectural visualization but are not solely established for either function.
For Data Quality, governed models provide the structural metadata from which rules can be derived. Keys support uniqueness; relationships support integrity; domains support validity; mandatory attributes support completeness; and standardized definitions support consistency.
The repository therefore becomes an important bridge between Data Architecture, Metadata Management, Data Governance, and Data Quality.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling Tools and Repositories; Enterprise Data Model; Conceptual/Logical/Physical Models; Chapter 13 - Structural Quality Rules.
NEW QUESTION # 103
Examples of transformation include:
Answer: A
Explanation:
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
NEW QUESTION # 104
A customer is shown as "Active" in the CRM but "Closed" in the billing system at the same point in time.
Both systems use the same customer identifier. Which Data Quality dimension is primarily affected?
Answer: A
Explanation:
The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.
The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation.
However, unless a legitimate business rule explains the difference, the two representations are inconsistent.
Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of
"Active" and "Closed," examine synchronization timing, and trace transformation or integration logic.
Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.
Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.
DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.
NEW QUESTION # 105
An organization changes the definition of "Net Revenue". Before implementing the change, it wants to identify every report, transformation, and analytical model that depends on the existing definition. Which capability is most important?
Answer: A
Explanation:
The required capability is Metadata lineage and impact analysis. Changing a business definition can affect far more than the glossary entry itself. The organization needs to know which physical fields, transformation rules, ETL jobs, calculations, reports, dashboards, analytical models, and downstream datasets depend on the current definition.
Lineage shows where data originates and how it flows and transforms. Impact analysis follows those relationships forward to determine what will be affected by a proposed change.
This prevents a common Data Quality failure in which a definition changes in one area while downstream systems continue applying the old interpretation. The result can be technically correct calculations that are semantically inconsistent.
A governed change should therefore update the business glossary, transformation metadata, Data Quality rules, reporting definitions, and affected documentation in a coordinated manner. Appropriate Data Stewards and Data Owners should approve the new meaning and effective date.
DAMA's framework describes Metadata Management as enabling understanding through definitions, lineage, and usage across systems, while the revised Chapter 13 explicitly strengthens the Data Quality-Metadata relationship.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Impact Analysis; Business Glossary; Data Quality Consistency.
NEW QUESTION # 106
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