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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
| Topic 2: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 3: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Topic 4: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Topic 5: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
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NEW QUESTION # 59
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
Answer: C
Explanation:
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
NEW QUESTION # 60
Examples of transformation include:
Answer: E
Explanation:
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
NEW QUESTION # 61
A customer's date of birth is stored as 12/04/1980. The value conforms to the database datatype and accepted date format, but the customer's verified date of birth is 21/04/1980. Which Data Quality dimension is primarily violated?
Answer: C
Explanation:
The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real- world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.
This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible-for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.
The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.
The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.
NEW QUESTION # 62
A Data Quality rule has been implemented and the defect rate has fallen below the agreed threshold. What activity should continue?
Answer: D
Explanation:
The organization should continue monitoring the quality metric. A successful remediation does not guarantee that the underlying process will remain controlled indefinitely.
Systems change, source applications are upgraded, personnel and suppliers change, reference values evolve, interfaces are modified, and business rules are revised. Any of these events can cause previously corrected defects to reappear.
A mature Data Quality lifecycle therefore treats improvement as continuous rather than as a one-time cleansing exercise. DAMA's revision of Chapter 13 specifically clarifies the Data Quality Improvement Lifecycle and links it to established continuous-improvement cycles.
Monitoring should confirm that the result remains within agreed thresholds and should provide trend information so deterioration can be detected before it creates significant business impact.
The frequency of monitoring may be reduced after a process demonstrates sustained stability, but the decision should be risk-based. Critical Data Elements generally justify stronger ongoing oversight.
The rule, threshold, owner, and measurement method should remain documented as metadata.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Control; Continuous Improvement; Metrics; Thresholds; Data Quality Scorecards.
NEW QUESTION # 63
Adoption of a Data Governance program is most likely to succeed:
Answer: E
Explanation:
DAMA-DMBOK2 recommends an incremental implementation strategy for Data Governance because governance introduces changes in accountability, decision rights, standards, processes, and organizational behaviour. Attempting an enterprise-wide implementation immediately creates unnecessary cultural and operational risk.
The implementation guidance is explicit that most governance rollout strategies are incremental, commonly beginning with a significant initiative such as Master Data Management or with a particular region, business unit, or domain. Enterprise-wide deployment as the initial step is described as uncommon.
Incremental implementation provides several control advantages. The organization can validate roles and decision mechanisms, establish stewardship practices, prove business value, refine policies, develop metrics, and correct weaknesses before expanding the governance framework. This aligns closely with Data Quality management, where organizations normally prioritize Critical Data Elements and high-value domains rather than attempting to measure and remediate every data element simultaneously.
Executive sponsorship is important, but a mandate alone does not produce sustainable adoption. Likewise, individual leadership charisma cannot substitute for operating processes, accountability, metrics, and change management. A short consultant-driven exercise also conflicts with DAMA's treatment of governance as an ongoing organizational capability.
Incremental rollout therefore offers the strongest mechanism for embedding governance permanently while demonstrating measurable value.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Implementation Guidelines; Organization and Culture; Adjustment and Communication; Chapter 13 - Data Quality Improvement and Governance.
NEW QUESTION # 64
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