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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - ISO 8000 (Data Quality) - Other Industry Standards |
| Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Metrics and KPIs - Benchmarking - Profiling Techniques |
| Data Quality Improvement | 20% | - Data Enrichment - Data Cleansing Techniques - Standardization - Validation Rules |
| Data Quality Governance | 15% | - Policies and Procedures - Data Stewardship - Continuous Monitoring - Data Quality Roles and Responsibilities |
| Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle - Definition of Data Quality |
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NEW QUESTION # 110
The implementation of data architecture exposes the transformation of data as it moves across the landscape.
A common name for this concept is:
Answer: B
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 # 111
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Answer: B
Explanation:
The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.
This reasoning is deceptive. The acquisition cost of storage is only one component of total information- management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.
From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.
A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 - Data Quality and Obsolete Data.
NEW QUESTION # 112
A relationship that allows an address to be used by multiple people, and each person can have multiple addresses, can be resolved:
Answer: E
Explanation:
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.
NEW QUESTION # 113
A Data Quality team has identified 500 defects across several domains. Which factor should have the strongest influence on remediation priority?
Answer: C
Explanation:
Remediation should principally be prioritized according to business impact and risk. Not all defects have equivalent consequences, even when they occur at similar frequencies.
A defect affecting regulatory reporting, customer payments, safety-critical operations, executive reporting, or high-value master data may require immediate remediation. A larger number of defects affecting a low- impact optional field may legitimately receive lower priority.
A robust prioritization model may consider financial loss, regulatory exposure, operational disruption, customer impact, reputational damage, number of dependent systems, recurrence rate, remediation cost, and whether a Critical Data Element is involved.
This risk-based approach prevents Data Quality programs from becoming simple defect-count reduction exercises. The objective is not merely to maximize the number of corrected records but to improve fitness for purpose where poor data creates material consequences.
Governance should approve prioritization criteria and resolve conflicts where different business areas assign different importance to the same issue. Metadata and lineage provide evidence about downstream dependencies and affected processes.
DAMA's revision of Chapter 13 adds a clearer Critical Data Element concept and clarifies responsibility within the Data Quality Improvement Lifecycle, reinforcing risk-based prioritization.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Issue Prioritization; Business Impact; Critical Data Elements; Risk; Remediation.
NEW QUESTION # 114
Three common interaction models for data integration are:
Answer: B
Explanation:
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.
NEW QUESTION # 115
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