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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Topic 2: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Topic 3: Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Topic 4: Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Topic 5: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
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NEW QUESTION # 14
A company requires every system to use the same ISO country-code list managed centrally. This is primarily an example of:
Answer: C
Explanation:
This is Reference Data Management. Country codes represent a controlled set of values used to classify or contextualize other data. Managing them centrally helps ensure that all systems interpret the same codes consistently.
Without centralized governance, one system might use GB, another UK, and another proprietary numeric values. Those differences create mapping complexity, failed integrations, inaccurate aggregation, and inconsistent reporting.
Reference Data Management establishes authoritative sources, stewardship, allowed values, mappings, effective dates, distribution processes, and change control. DAMA's public framework describes Reference and Master Data Management as ensuring consistency in core shared information across organizational units.
The Data Quality dimensions most directly supported are Validity and Consistency. Validity ensures values belong to an approved domain; consistency ensures equivalent concepts are represented compatibly across systems.
Metadata should document the standard, meaning, owner, and version of each reference domain.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference Data; Controlled Code Sets; Authoritative Sources; Chapter 13 - Validity and Consistency.
NEW QUESTION # 15
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Answer: D
Explanation:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
NEW QUESTION # 16
Which of the following activities is NOT a way that enterprise data architecture influences the scope boundaries of projects?
Answer: E
Explanation:
Ensuring enterprise business processes are effectively documented is not primarily a Data Architecture mechanism for defining project scope boundaries. That responsibility belongs more directly to Business Architecture, process management, and business-analysis disciplines.
DAMA-DMBOK2 explains that Enterprise Data Architecture influences projects by defining enterprise data requirements, reviewing project data designs, determining lineage impacts, controlling unnecessary replication, enforcing Data Architecture standards, and guiding technology and renewal decisions.
Consequently, options A, B, D, and E all represent valid ways architecture can constrain or guide project scope. A design review checks that local solutions remain compatible with enterprise strategy. Standards prevent individual projects from creating incompatible structures. Replication controls limit uncontrolled proliferation of redundant data. Enterprise data requirements ensure that projects account for information needs beyond their immediate application boundaries.
Documenting business processes remains important because processes produce and consume data, but comprehensive process documentation is not itself a primary Enterprise Data Architecture scope-control activity.
From a Data Quality perspective, architectural influence prevents project-level decisions from creating duplicated authoritative sources, inconsistent definitions, undocumented lineage, or uncontrolled transformations.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Manage Enterprise Requirements within Projects; Architecture Governance; Project Scope Boundaries; Data Replication; Chapter 13 - Consistency and Lineage.
NEW QUESTION # 17
The Data Architecture workstream on results produces:
Answer: C
Explanation:
Within DAMA-DMBOK2, establishing a Data Architecture practice is divided into several complementary workstreams. The Results workstream is specifically responsible for producing Data Architecture artifacts within an overall roadmap. The other answer choices correspond to different aspects of the architecture practice rather than to the Results workstream itself.
DMBOK2 distinguishes the workstreams clearly: Strategy selects frameworks and develops the roadmap; Acceptance and Culture addresses awareness and behavioural change; Organization assigns architecture accountability and responsibility; Working Methods establish practices and coordinate architecture work with development initiatives; and Results produces the architectural artifacts that collectively realize the roadmap.
Typical artifacts include enterprise data models, high-level data-flow designs, architectural standards, transition-state specifications, and related metadata. These artifacts represent both current and target architectural states and allow individual projects to align with enterprise direction.
The Data Quality connection is substantial because architectural artifacts identify authoritative sources, integration points, critical data flows, entities, relationships, and dependencies. Those structures provide the context needed to establish validity, integrity, consistency, lineage, and quality-monitoring requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Establish Data Architecture Practice; Results Workstream; Data Architecture Artifacts; Architecture Roadmap; Chapter 13 - Data Quality Dependencies.
NEW QUESTION # 18
The implementation of data architecture exposes the transformation of data as it moves across the landscape.
A common name for this concept is:
Answer: D
Explanation:
The concept described is Data Lineage. Data lineage records how data originates, moves, transforms, and is consumed across the information landscape. DAMA-oriented architecture guidance explicitly links implementation of Data Architecture with visibility into transformations occurring as data traverses systems and identifies this as lineage.
Lineage can operate at several levels. At a high level, it may identify that customer information moves from CRM into an integration platform, Master Data hub, warehouse, and reporting environment. At a detailed level, it may show that a particular report column derives from a specific source attribute through documented calculations, mappings, and transformation rules.
This capability is fundamental to Data Quality. When an incorrect result appears in a report, lineage helps analysts trace the defect upstream to the point where it originated or was introduced. It also supports root- cause analysis, impact assessment, regulatory traceability, change management, and reconciliation.
Metadata Management provides the repository structures needed to capture and maintain lineage. Data Governance determines which lineage must be documented and who is accountable for maintaining it.
ETL is one mechanism through which data may move and transform, but ETL is not the architectural concept describing the end-to-end history and derivation of the data.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Chapter 11 - Metadata Management; Data Lineage; Chapter 13 - Root-Cause Analysis and Data Quality Traceability.
NEW QUESTION # 19
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