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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Improvement | 20% | - Root cause analysis - Monitoring and continuous improvement - Data cleansing and standardization |
| Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
| Data Quality Strategy and Governance | 20% | - Integration with data governance - Data quality strategy and policy - Roles and responsibilities |
| Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
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NEW QUESTION # 26
The primary reason for identifying Critical Data Elements is to:
Answer: C
Explanation:
Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.
Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.
The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.
Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.
Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.
Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.
NEW QUESTION # 27
The implementation of a 'Super Type - Sub Type' structure can use the following 2 options:
Answer: E
Explanation:
DAMA-DMBOK2 identifies two recognized approaches for resolving logical supertype-subtype abstractions when moving into physical database design: Subtype Absorption and Supertype Partition.
With Subtype Absorption, attributes belonging to subtype entities are incorporated into the table representing the supertype. Attributes that apply only to particular subtypes may consequently be nullable. This approach reduces the number of physical tables but can introduce sparsity and requires explicit rules to ensure subtype- specific attributes remain semantically valid.
With Supertype Partition, the attributes belonging to the supertype are carried into separate physical tables representing each subtype. This can simplify subtype-specific processing but introduces duplication of common structures and requires disciplined metadata and modelling control.
The choice has direct consequences for Data Quality. Subtype absorption requires validity and completeness rules to distinguish legitimate nulls from missing data. Supertype partition requires consistency controls to ensure shared attributes retain identical definitions and constraints across subtype tables. Metadata repositories should document subtype discriminators, attribute definitions, constraints, and inheritance rules.
DMBOK2 therefore treats the transformation as a deliberate physical modelling decision rather than the generic "merge/split" terminology presented in the distractors.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Physical Data Modeling; Resolve Logical Abstractions; Chapter 13 - Validity, Completeness and Consistency; Metadata Management.
NEW QUESTION # 28
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 # 29
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
Answer: B
Explanation:
The organization should determine the root cause and authoritative remediation point before multiple teams independently modify downstream copies.
If a defective value originates in a source system and is distributed to five downstream applications, correcting those five copies independently may provide temporary relief but leaves the upstream defect intact.
The incorrect value may simply be redistributed again.
Lineage should be used to trace the defect through source systems, transformations, integration layers, Master Data hubs, warehouses, and reports. Once the origin is understood, governance can determine which system or process is authoritative and where correction should occur.
This approach minimizes inconsistent local fixes and reduces the risk that different teams apply incompatible interpretations of the same issue.
DAMA's Chapter 13 revision explicitly strengthens the relationship between Data Quality and Metadata Management, Reference/Master Data Management, Modeling, and Data Integration. Those connections are precisely what enable cross-system issue resolution.
Downstream correction may still be required after the authoritative source is fixed, but it should be coordinated.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Issue Management; Metadata Lineage; Authoritative Sources; Data Integration.
NEW QUESTION # 30
Three common interaction models for data integration are:
Answer: A
Explanation:
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
NEW QUESTION # 31
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