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

The correct definition is the fundamental organisation of a system, and the principles governing its design and evolution. DAMA-DMBOK2 incorporates the ISO/IEC 42010 architectural concept when establishing the theoretical basis for Data Architecture.

The fuller ISO formulation describes architecture in terms of the fundamental organization of a system embodied in its components, their relationships to one another and to the environment, together with the principles governing design and evolution. DAMA uses this concept to distinguish true architecture from a collection of diagrams or implementation specifications.

Option E is therefore incomplete: architectural artifacts document architecture, but the artifacts themselves are not the architecture. Option C focuses too narrowly on implementation and maintenance, while options B and D describe potential objectives or constraints rather than the definition.

Applied to enterprise data, this means architecture establishes the high-level organization of data assets, information flows, platforms, relationships, and guiding principles by which the data environment evolves.

Data Quality benefits because architectural principles establish where authoritative information originates, how it moves, how duplication is controlled, and where governance and quality controls must operate.

Reference Topics: DAMA-DMBOK2 Chapter 4 — Architecture Definition; ISO/IEC 42010; Enterprise Data Architecture; Architectural Principles; Chapter 13 — Quality by Design.

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A fishbone diagram, also known as an Ishikawa or cause-and-effect diagram, is used to organize and explore potential root causes of a problem.

For recurring missing email addresses, categories might include People, Process, Technology, Data, Policy, Training, and External Sources. Potential causes could include optional form design, unclear business requirements, API mapping defects, missing supplier data, or inconsistent onboarding procedures.

The technique helps prevent teams from prematurely assuming that the most visible symptom is the actual cause. It encourages structured investigation across multiple dimensions of the process.

Once hypotheses are identified, evidence should be collected to confirm or reject them. The diagram itself does not prove causation.

In Data Quality programs, root-cause analysis is essential because repeated cleansing without process correction allows defects to recur. DAMA's Chapter 13 revision explicitly retains and strengthens the treatment of common causes and the improvement lifecycle.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Root-Cause Analysis; Cause-and-Effect Diagram; Issue Management; Continuous Improvement.

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DAMA-DMBOK2 organizes Data Handling Ethics around three fundamental considerations: impact on people, potential for misuse, and the economic value of data. The text explains that data frequently represents individuals and is used in decisions that materially affect them; consequently, organizations have an ethical responsibility to ensure that data is reliable and appropriately managed. It also recognizes that data can be intentionally or unintentionally misused and that its economic value creates questions concerning ownership, access, rights, and permitted use.

These concepts directly intersect with Data Quality. Incorrect or misleading data may result in decisions that adversely affect customers, employees, patients, citizens, or other stakeholders. For that reason, accuracy, completeness, transparency, provenance, and appropriate interpretation are not merely technical characteristics—they can have ethical consequences.

Governance operationalizes these ethical principles through policies, decision rights, stewardship responsibilities, access rules, monitoring, and escalation procedures. Metadata Management supports transparency by documenting meaning, provenance, classification, and permitted use. Master Data Management also has an ethical dimension because consolidated person or customer records may create significant consequences when matching or identity-resolution errors occur.

Privacy and security are important controls, but they do not constitute DAMA's complete set of core ethical concepts.

Reference Topics: DAMA-DMBOK2 Chapter 2 — Data Handling Ethics; Impact on People; Potential for Misuse; Economic Value of Data; Chapter 13 — Accuracy, Reliability and Governance.

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A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA-DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.

A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.

This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures. Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.

Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.

Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.

Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence — Maintain Data Products; Release Management; Incremental Development; Chapter 13 — Quality Monitoring and Change Control.

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Compound authorization groups allow access rights from multiple groups or roles to be combined so that an individual's effective permissions can be configured with greater precision. The user may inherit one set of privileges from an organizational role, another from a functional group, and additional permissions from a specialized operational responsibility. The resulting authorization can therefore be more granular than assigning every privilege individually.

DAMA's Data Security framework separates authentication—establishing identity—from authorization, which determines what an authenticated identity is permitted to access or perform. Group-based authorization is a practical mechanism for implementing those decisions. CDMP-oriented references for this specific item identify “precisely configure an individual's access to a system” as the intended answer.

The principle must nevertheless be governed carefully. Excessive overlapping group memberships can make effective permissions difficult to review and can create segregation-of-duties conflicts. Periodic access reviews, least-privilege design, role ownership, and audit trails are therefore necessary complementary controls.

Encryption addresses confidentiality of data in transit or storage and is unrelated to how permissions are assigned. Audit preparation may benefit from structured authorization, but it is not the principal purpose.

Reference Topics: DAMA-DMBOK2 Chapter 7 — Authorization; Access Control; Least Privilege; Security Roles; Audit and Monitoring.

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When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.

DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.

Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.

Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.

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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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The appropriate dimension is Reasonableness. A value may comply with technical and business-domain constraints while still being implausible in its operating context. An individual customer purchasing 750,000 office chairs is technically possible, but it is sufficiently unusual that it should trigger review.

Reasonableness controls evaluate whether values and combinations of values fall within credible expectations. These controls often depend on business context, historical behaviour, peer comparisons, statistical limits, or relationships between multiple fields.

The current DMBOK2 revision standardizes the term Reasonableness within its nine standard dimensions. The broader DMBOK2 dimension framework links reasonableness to whether data should be regarded as credible within the operational context.

Useful controls might compare order quantity against customer type, historical maximums, product availability, typical basket size, or statistical deviation from normal purchasing behaviour.

Reasonableness rules are especially valuable for identifying errors that conventional validation cannot detect. However, unusual data should not automatically be treated as incorrect; it should usually be flagged for investigation.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Reasonableness; Statistical Validation; Exception Detection; Business Rules.

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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.

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A candidate key is a minimal super key. A super key is any set of attributes sufficient to uniquely identify an entity instance, but it may contain attributes that are unnecessary for uniqueness. A candidate key removes that redundancy: if any attribute is removed from the candidate key, the remaining attributes no longer uniquely identify the entity.

DAMA-DMBOK2 makes this distinction explicitly: a candidate key is a minimal set of one or more attributes identifying an entity instance, and “minimal” means that no subset of the candidate key can perform the same unique-identification function.

Option B is incomplete because a set can uniquely identify an entity while still containing unnecessary attributes; that would qualify as a super key but not necessarily a minimal super key. A surrogate key is different: it is an artificial identifier introduced primarily for technical identification. Foreign-key composition and physical index structures likewise do not define candidate-key minimality.

This concept has direct Data Quality implications. Correctly defined candidate and primary keys support uniqueness and integrity, prevent duplicate entity instances, enable reliable referential relationships, and improve entity matching within Master Data Management. Key definitions should also be captured as structural metadata so profiling and quality rules can consistently test duplicate and orphan conditions.

Reference Topics: DAMA-DMBOK2 Chapter 5 — Data Modeling and Design; Keys; Candidate Keys; Chapter 13 — Uniqueness and Integrity; Metadata Management; Master Data Management.

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