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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
| Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Definition and importance of data quality - Data quality dimensions |
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
| Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Business value and ROI - Data quality tools and technologies |
| Data Quality Improvement | 20% | - Root cause analysis - Monitoring and continuous improvement - Data cleansing and standardization |
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NEW QUESTION # 12
What is a steward?
Answer: D
Explanation:
In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: "A steward is a person whose job it is to manage the property of another person." It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.
A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.
The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.
A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 - Data Quality Governance and Responsibilities.
NEW QUESTION # 13
In Data Modelling, the generalization of the concept of person and organisation into a party enables:
Answer: D
Explanation:
The Party pattern generalizes Person and Organization under a common supertype so that both can participate consistently in shared business relationships and processes. The major benefit is that persons and organizations can act in roles in agreements, share address structures, and participate in common interaction processes, making option E correct.
For example, a Customer, Supplier, Account Holder, Employee, or Contracting Party may be either a person or an organization. Without the Party abstraction, separate relationships may need to be constructed repeatedly for Person and Organization. With Party, common concepts such as Agreement, Address, Contact Point, Role, and Interaction can reference a single generalized entity.
This is particularly relevant to Master Data Management because Party is a common enterprise modelling pattern for Customer, Supplier, and related master-data domains. It supports entity resolution while preserving distinctions between individuals and organizations through subtype structures.
From a Data Quality perspective, the approach improves consistency by reducing duplicated relationship definitions and business rules. However, governance must still establish subtype-specific requirements-for example, organization registration identifiers versus personal names and dates of birth.
The abstraction is therefore primarily about reusable semantic relationships and roles, not security concealment or faster package implementation.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Generalization and Specialization; Party Model; Chapter 10 - Master Data; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 14
The Family Name of a Person is recorded in a system. The column name is Pname. Pname is an example of:
Answer: B
Explanation:
Pname is Metadata because it is the name assigned to a database column and therefore describes the structure in which the actual Family Name values are stored. A value such as Smith or Patel would be data; Pname is structural information describing that data.
DAMA-DMBOK2 defines Metadata broadly as information about data and data-related processes. Technical metadata includes physical database characteristics such as table names, column names, datatypes, lengths, indexes, constraints, and related structural properties. The interpretation of Pname as metadata is also consistent with the published version of this exact certification question.
The fact that Pname may be an unclear abbreviation does not change its classification. It may represent a poor naming convention from a usability or governance perspective, but technically it remains metadata. A stronger physical name might be FamilyName, while the metadata repository could additionally map that field to the authoritative business glossary term.
This distinction matters to Data Quality because rules are frequently attached to metadata objects. For example, the Family Name attribute might have completeness, permitted-character, maximum-length, and standardization requirements.
Effective Metadata Management connects the technical column to its business meaning, system lineage, stewardship, and applicable Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 11 - Technical Metadata; Database Metadata; Business Glossary; Chapter 13 - Data Quality Rules and Metadata.
NEW QUESTION # 15
Mapping requirements and rules for moving data from source to target enables:
Answer: B
Explanation:
Source-to-target mapping enables Transformation. DAMA-DMBOK2 treats mapping as closely synonymous with transformation because a mapping defines how data in one source structure will be converted into the structure, format, representation, or value required by the target.
A mapping specification typically identifies the source attribute, target attribute, extraction conditions, target population rules, intermediate staging transformations, calculations, lookup requirements, and any changes required to make the source data conform to the target representation. DMBOK2 specifically explains that mapping sources to targets involves defining the rules for transforming information from one location and format into another.
Extraction simply retrieves data from the source. Loading places data into the target. Transformation is the activity that applies structural, syntactic, semantic, or value-level modifications between those stages.
The Data Quality connection is substantial. Mappings may standardize dates, convert units, harmonize codes, resolve reference values, remove duplicates, or enforce business rules. If mapping metadata is incomplete or incorrect, the transformation process can introduce rather than correct quality defects.
For this reason, source-to-target mapping should be governed, version-controlled, documented as metadata, traceable through lineage, and validated against agreed business definitions and Data Quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Map Data Sources to Targets; Transformation; ETL
/ELT; Metadata Lineage; Chapter 13 - Data Cleansing and Standardization.
NEW QUESTION # 16
The best way to manage a data architecture roadmap is by using:
Answer: A
Explanation:
Integrated strategic reviews provide the strongest mechanism for managing a Data Architecture roadmap because the roadmap must remain synchronized with enterprise strategy, business capability priorities, other architecture domains, projects, resources, and changing dependencies.
DAMA-DMBOK2 describes the Enterprise Data Architecture roadmap as the three-to-five-year path by which the target architecture becomes reality. Crucially, it states that this roadmap must be integrated into the overall Enterprise Architecture roadmap, including milestones, required resources, cost estimates, and business-capability workstreams. The roadmap should also reflect business requirements, current conditions, technical assessments, and organizational maturity.
This means roadmap management cannot sensibly be reduced to a once-a-year exercise. Strategic conditions, technology decisions, project sequencing, dependencies, and regulatory requirements may change throughout the roadmap period. Integrated reviews allow those changes to be evaluated in the context of the wider enterprise architecture rather than independently.
Senior-management support is necessary for authority and funding, while peer reviews and results evaluation are useful control activities. None, however, provides the same integrated strategic mechanism for keeping architectural direction aligned with enterprise priorities.
From a Data Quality perspective, such reviews also ensure that architecture changes preserve authoritative sources, lineage, integration controls, and enterprise quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Develop a Roadmap; Enterprise Architecture Integration; Data Dependencies; Lifecycle Reviews; Architecture Governance.
NEW QUESTION # 17
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