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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Data Quality Concepts and Principles | 15% | - Data quality dimensions - Data quality in DMBOK framework - Definition and importance of data quality |
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Integration with data governance - Roles and responsibilities |
| Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
| Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Data quality tools and technologies - Business value and ROI |
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102. Frage
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
Antwort: D
Begründung:
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
103. Frage
The primary reason for identifying Critical Data Elements is to:
Antwort: C
Begründung:
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.
104. Frage
Data modelling tools and model repositories are necessary for:
Antwort: D
Begründung:
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.
105. Frage
A financial transaction is captured correctly at 9:00 AM but does not become available to the fraud- monitoring system until 6:00 PM, although the business requirement is availability within five minutes.
Which Data Quality dimension is primarily violated?
Antwort: A
Begründung:
The primary failure is Timeliness. The transaction may be completely accurate and complete, but it is not available within the period required by the consuming business process.
Timeliness evaluates whether data is available when needed for its intended use. The relevant threshold must therefore come from the business requirement rather than from an arbitrary technical target. In this scenario, the fraud-monitoring process requires the transaction within five minutes, while delivery occurs approximately nine hours later.
The root cause could exist in extraction frequency, integration queues, batch processing, network delays, source-system availability, or downstream ingestion. Lineage and operational metadata should be used to identify where the latency occurs.
Timeliness must also be distinguished from Currency. Currency asks whether information reflects a sufficiently recent real-world state; Timeliness asks whether data is delivered or available within the required period. A current transaction that arrives too late can therefore fail Timeliness even though the underlying value accurately represented reality when captured.
DAMA's revised DMBOK2 Chapter 13 recognizes both Timeliness and Currency as separate standard dimensions.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Timeliness; Currency; Data Quality Requirements; Data Integration; Operational Monitoring.
106. Frage
A search engine database is populated by a web crawler or spider software that usually processes as:
Antwort: A
107. Frage
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