DAMA DAMA Data Quality Specialist DQ-1220 Exam Dumps: Updated Questions & Answers (October 2026)

The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.

Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.

The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.

This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.

The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.

Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.

Reference Topics: DAMA-DMBOK2 — Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.

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The required capability is Metadata lineage and impact analysis. Changing a business definition can affect far more than the glossary entry itself. The organization needs to know which physical fields, transformation rules, ETL jobs, calculations, reports, dashboards, analytical models, and downstream datasets depend on the current definition.

Lineage shows where data originates and how it flows and transforms. Impact analysis follows those relationships forward to determine what will be affected by a proposed change.

This prevents a common Data Quality failure in which a definition changes in one area while downstream systems continue applying the old interpretation. The result can be technically correct calculations that are semantically inconsistent.

A governed change should therefore update the business glossary, transformation metadata, Data Quality rules, reporting definitions, and affected documentation in a coordinated manner. Appropriate Data Stewards and Data Owners should approve the new meaning and effective date.

DAMA's framework describes Metadata Management as enabling understanding through definitions, lineage, and usage across systems, while the revised Chapter 13 explicitly strengthens the Data Quality–Metadata relationship.

Reference Topics: DAMA-DMBOK2 — Metadata Management; Data Lineage; Impact Analysis; Business Glossary; Data Quality Consistency.

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GARP stands for Generally Accepted Recordkeeping Principles. ARMA International developed the framework to describe the characteristics of an effective records and information-management program. The principles address accountability, transparency, integrity, protection, compliance, availability, retention, and disposition.

These principles align closely with DAMA-DMBOK2's treatment of Document and Content Management. Information must be created, organized, protected, maintained, retrieved, retained, and ultimately disposed of according to business, legal, regulatory, and historical requirements. Records management is therefore not simply about long-term storage. It establishes controls throughout the information lifecycle.

For example, the Integrity principle addresses the authenticity and reliability of records. Availability requires information to be retrievable accurately and efficiently. Retention requires organizations to keep records for justified periods, while Disposition governs appropriate final handling after those obligations expire.

These controls interact directly with Data Quality. Records that cannot be found, trusted, interpreted, or demonstrated to be authentic are not fit for their intended purpose even if they physically exist.

Metadata Management supplies classification, retention, provenance, ownership, and lifecycle metadata necessary to implement these principles consistently.

Reference Topics: DAMA-DMBOK2 — Document and Content Management; Records Management; GARP; Retention; Disposition; Integrity; Metadata Management.

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DAMA-DMBOK2 assigns primary responsibility for business glossary content to Business Data Stewards. Business Data Stewards are typically subject-matter experts who understand how data is defined, created, interpreted, and consumed within their business domain. DMBOK2 explicitly states that Data Stewards are generally responsible for business glossary content and identifies Business Data Stewards as professionals who work with stakeholders to define and control data.

A business glossary is not simply a technical dictionary. It establishes agreed business terminology, definitions, synonyms, business rules, responsible stewards, and relationships between business concepts. This makes stewardship involvement essential because definitions must reflect operational and business meaning rather than merely database structures.

Data Architects may contribute candidate definitions and structural context from subject-area and conceptual models, but they do not generally own the business meaning. Business users provide valuable input, while Coordinating Data Stewards help reconcile definitions across domains, yet the normal accountability remains with Business Data Stewards.

This relationship is particularly important for Data Quality. Quality rules depend on unambiguous definitions of data elements. If “Customer,” “Active Account,” or “Order Date” has inconsistent meanings, measurements of completeness, accuracy, and validity cannot be consistently interpreted.

Reference Topics: DAMA-DMBOK2 Chapter 3 — Develop a Business Glossary; Business Data Stewardship; Metadata Management; Chapter 13 — Data Quality Rules and Business Definitions.

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A Business Continuity Plan is fundamentally concerned with maintaining essential business operations when normal services are disrupted. Therefore, the correct response is that it outlines how the business will continue operating during an unplanned disruption.

Within the DAMA-DMBOK2 framework, continuity concerns interact particularly with Data Governance, Data Storage and Operations, Data Security, and operational metadata. The DMBOK2 index explicitly identifies Business Continuity Plans and associates them with operational resilience and recovery concepts. A continuity plan must address how critical processes, information assets, personnel, technology, dependencies, and recovery arrangements will sustain or restore required business capability. The established definition of a BCP likewise centers on continued operation during unplanned service disruption.

Option E is incomplete: identifying potential disruptions belongs to risk assessment and continuity planning, but merely listing disruptions does not explain how operations will continue. Options B and D concern communications rather than continuity itself.

From a Data Quality perspective, continuity controls also protect availability, timeliness, integrity, and reliability. Recovery arrangements must ensure that restored data is complete, current enough for business use, and reconciled against authoritative sources after disruption.

Reference Topics: DAMA-DMBOK2 — Data Governance; Data Storage and Operations; Business Continuity; Disaster Recovery; Backup and Recovery; Chapter 13 — Timeliness, Integrity and Fitness for Purpose.

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The mechanism is designed to meet confidentiality and privacy requirements. Customer bank-account details constitute sensitive financial information. Inspecting outgoing email for such values is a form of Data Loss Prevention control intended to identify or prevent inappropriate disclosure before sensitive information leaves the organization's controlled environment.

DAMA-DMBOK2 treats confidentiality and privacy as fundamental Data Security requirements. Its electronic-communication guidance warns that restricted or confidential information should not be sent through insecure communication channels because messages can be intercepted, forwarded, or otherwise disclosed after leaving the sender's control. DAMA International's own privacy practices likewise classify bank-account and similar financial information as protected personal/financial data requiring security safeguards against unauthorized disclosure.

The other responses concern transparency, definitions, user experience, or availability and do not address the security objective demonstrated in the scenario.

Data Governance determines the classifications and policies governing such information; Metadata Management can record sensitivity classifications against physical data elements; and security technology then enforces the resulting controls.

Data Quality remains relevant because confidentiality controls must distinguish genuinely sensitive values accurately. Poor classification or inaccurate detection rules may either expose protected information or unnecessarily block legitimate communication.

Reference Topics: DAMA-DMBOK2 Chapter 7 — Confidentiality and Privacy; Electronic Communication Security; Sensitive Data; Data Loss Prevention; Data Governance.

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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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The correct long-term response is to remove the root cause in the source or integration process. Repeated warehouse cleansing treats symptoms. If the defect is recreated every night, operational costs continue and downstream systems remain exposed until the cleansing process executes.

DAMA Data Quality practice emphasizes sustained improvement rather than repeated correction. The quality lifecycle therefore includes identifying defects, determining business impact, analyzing root causes, implementing corrective actions, and monitoring results. The current Chapter 13 revision specifically strengthens clarification of the Data Quality Improvement Lifecycle and responsibilities within it.

The underlying cause might be weak source validation, an incorrect source-to-target mapping, outdated Reference Data, transformation logic, or missing governance over postal-code rules.

Cleansing remains appropriate where historical data must be repaired or immediate downstream protection is required. However, preventive control should be introduced as close to the creation point as practical.

Metadata and lineage help trace the postal code through the data flow, while governance establishes who has authority to change the offending process.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Root-Cause Analysis; Remediation; Prevention; Cleansing; Data Integration and Lineage.

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Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.

Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.

The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.

Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.

Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.

Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.

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This is Data Enrichment. Enrichment enhances existing records by adding useful information derived from internal or external sources. The original postal address remains intact, while geographic coordinates are appended to make the customer record more useful for location analysis, logistics, service coverage, route planning, or segmentation.

Enrichment differs from cleansing. Cleansing corrects or standardizes defective data; enrichment adds information that was not previously present. A process may perform both—for example, standardizing an address before passing it to a geocoding service.

External enrichment introduces governance and quality considerations. The organization should assess source reliability, licensing and permitted usage, refresh frequency, match confidence, geographical precision, lineage, and whether enrichment values should be treated as authoritative or derived.

Metadata should record that latitude and longitude were obtained from an external geocoding process rather than directly supplied by the customer. Quality metrics may measure match rate, confidence, completeness, or positional accuracy.

DAMA identifies Data Quality Management as including error detection, cleansing, and enrichment strategies, while Metadata Management supports understanding of lineage, definitions, and usage.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Data Enrichment; Cleansing; Profiling; Metadata Lineage; External Data Sources.

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