DAMA Updated DQ-1220 Exam Questions and Answers by ianis

The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.

The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation. However, unless a legitimate business rule explains the difference, the two representations are inconsistent.

Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of "Active" and "Closed," examine synchronization timing, and trace transformation or integration logic. Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.

Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.

DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.

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DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.

In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.

The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and-spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.

DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.

Reference Topics: DAMA-DMBOK2 Chapter 8 — Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.

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Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.

The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.

The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship. Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.

Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.

Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.

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The primary issue is Data Integrity, specifically referential integrity. The Orders record references a Customer_ID that does not correspond to an existing Customer record, meaning the relationship defined by the data model has been violated.

Integrity concerns whether structural relationships and constraints among data elements remain valid. In relational environments, this commonly includes primary-key uniqueness, foreign-key relationships, mandatory relationships, and cardinality constraints.

A customer identifier could be syntactically valid and populated, yet still fail integrity because no corresponding parent entity exists. This illustrates why integrity is different from completeness or validity. The DMBOK2 dimension model explicitly associates integrity with unique identifiers, cardinality, and referential integrity concepts.

Remediation should determine why the orphan record occurred. Potential causes include incorrect load sequencing, deletion of the parent record, integration failure, transformation defects, or absence of database constraints.

Metadata and Data Modeling establish the expected relationship; Data Quality controls then measure whether operational data conforms to it.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Data Integrity; Referential Integrity; Chapter 5 — Relationships and Keys; Data Integration Controls.

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