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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Data Quality Improvement | 20% | - Data cleansing and standardization - Monitoring and continuous improvement - Root cause analysis |
| Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Data profiling techniques - Assessment methods and tools |
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality in DMBOK framework - Data quality dimensions |
| Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Data quality in lifecycle management - Business value and ROI |
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NEW QUESTION # 75
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: B
Explanation:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
NEW QUESTION # 76
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 # 77
A minimal super key is:
Answer: B
Explanation:
A candidate key is a minimal super key. A super key is any set of attributes sufficient to uniquely identify an entity instance, but it may contain attributes that are unnecessary for uniqueness. A candidate key removes that redundancy: if any attribute is removed from the candidate key, the remaining attributes no longer uniquely identify the entity.
DAMA-DMBOK2 makes this distinction explicitly: a candidate key is a minimal set of one or more attributes identifying an entity instance, and "minimal" means that no subset of the candidate key can perform the same unique-identification function.
Option B is incomplete because a set can uniquely identify an entity while still containing unnecessary attributes; that would qualify as a super key but not necessarily a minimal super key. A surrogate key is different: it is an artificial identifier introduced primarily for technical identification. Foreign-key composition and physical index structures likewise do not define candidate-key minimality.
This concept has direct Data Quality implications. Correctly defined candidate and primary keys support uniqueness and integrity, prevent duplicate entity instances, enable reliable referential relationships, and improve entity matching within Master Data Management. Key definitions should also be captured as structural metadata so profiling and quality rules can consistently test duplicate and orphan conditions.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Keys; Candidate Keys; Chapter 13 - Uniqueness and Integrity; Metadata Management; Master Data Management.
NEW QUESTION # 78
Who should normally define the business meaning and acceptable quality requirements for a critical customer attribute?
Answer: B
Explanation:
A Business Data Steward, working with relevant business stakeholders and governance bodies, is the appropriate role to define or coordinate business meaning and quality expectations.
Quality requirements cannot be derived solely from database structures. The organization must understand what the attribute means, how it is used, what values are acceptable, which source is authoritative, and what consequences arise when it is wrong.
Data Stewards bridge business knowledge and formal Data Management controls. They commonly participate in maintaining glossary definitions, defining business rules, resolving issues, clarifying ownership, and establishing Data Quality expectations.
Technical specialists contribute implementation knowledge. A DBA may identify datatype constraints, while an integration specialist can implement transformations. Neither should independently determine business meaning.
DAMA's public framework describes Metadata Management as supporting definitions, lineage, and governance, while Reference and Master Data Management ensures consistency in shared core entities.
The strongest operating model therefore combines business accountability with technical execution.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Chapter 11 - Business Metadata; Chapter 13 - Data Quality Requirements; Critical Data Elements.
NEW QUESTION # 79
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: A
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 80
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