Pass the DAMA CDMP DQ-1220 Questions and answers with Dumpstech

The scenario describes a many-to-many relationship: one Person may use several Addresses, while one Address may be associated with several Persons. In relational modelling, this should be resolved using an associative entity—here, Person Address Usage—between Person and Address. The original many-to-many relationship is thereby replaced with two one-to-many relationships. DAMA-oriented references identify this approach directly under the addition of associative entities.

The associative entity normally contains foreign keys referencing both parent entities and may also contain attributes describing the association itself, such as address type, usage purpose, effective date, end date, or primary-address indicator.

Simply modifying the primary keys of Person or Address does not resolve the semantic relationship. Changing foreign-key role names also changes terminology rather than cardinality. The term “partnership entity” is not the appropriate modelling construct.

This design has important Data Quality consequences. It permits explicit integrity constraints between the association and both parent entities, prevents ambiguous repeated columns, and allows the organization to validate rules such as valid effective periods and permitted address-use types.

Reference Topics: DAMA-DMBOK2 Chapter 5 — Relationships; Cardinality; Associative Entities; Resolving Many-to-Many Relationships; Chapter 13 — Referential Integrity and Consistency.

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In a data model, entities are linked by relationships. An entity represents a distinguishable business concept or thing about which the organization stores information, while a relationship expresses how one entity is associated with another.

For example, a Customer places an Order, an Order contains Order Lines, and a Product appears on an Order Line. Relationships therefore capture business semantics rather than merely physical implementation details. Cardinality is an important characteristic of a relationship because it specifies how many instances of one entity may or must be associated with instances of another; however, cardinality is not itself the general mechanism by which entities are linked. DAMA-oriented modelling material identifies relationships as the correct linkage concept.

Indexes and triggers are physical database mechanisms. Processes describe business activities and may interact with entities, but they do not define entity-to-entity structure within a data model.

The Data Quality implications are significant. Properly defined relationships establish expectations for referential integrity. For example, an Order referencing a nonexistent Customer indicates an integrity defect. Profiling can test orphaned foreign keys, invalid relationship cardinalities, and inconsistent associations.

Relationships and their cardinalities should therefore be documented as metadata and enforced through appropriate database, application, or quality controls.

Reference Topics: DAMA-DMBOK2 Chapter 5 — Entities, Relationships and Cardinality; Logical Data Modeling; Chapter 13 — Integrity and Consistency; Metadata Management.

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A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.

For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.

The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.

DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.

Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.

Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.

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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.

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DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.

Complete means required shared entities and attributes are sufficiently populated for their intended use. Consistent means equivalent data has compatible meaning and representation wherever it is consumed. Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.

These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.

DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.

Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.

Reference Topics: DAMA-DMBOK2 Chapter 10 — Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 — Completeness, Consistency and Currency.

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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.

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