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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Improvement | 20% | - Validation Rules - Data Cleansing Techniques - Standardization - Data Enrichment |
| Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Metrics and KPIs - Profiling Techniques - Benchmarking |
| Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Continuous Monitoring - Data Stewardship - Policies and Procedures |
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - ISO 8000 (Data Quality) - Other Industry Standards |
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NEW QUESTION # 113
A company has five systems that can update a customer's legal name, and no system is designated as authoritative. What is the greatest Data Quality risk?
Answer: D
Explanation:
The principal risk is inconsistent customer-name values across systems. When multiple applications can independently update the same business fact without an authoritative source or coordinated Master Data process, conflicting representations are likely to emerge.
The problem is architectural and governance-related as much as it is a Data Quality issue. The organization must determine where the authoritative value is created, who is responsible for approving changes, how updates propagate, and how conflicts are resolved.
DAMA's public framework emphasizes a single trusted or consistent approach to shared core entities through Reference and Master Data Management.
The relevant Data Quality dimensions include Consistency, Accuracy, Currency, and potentially Uniqueness.
An application may hold a technically valid legal name but still contain an outdated or conflicting value.
Metadata and lineage should document which systems create, master, distribute, or merely consume the attribute.
Reference Topics: DAMA-DMBOK2 - Authoritative Sources; Master Data Management; Data Architecture; Consistency; Lineage; Stewardship.
NEW QUESTION # 114
A source application is modified so that a mandatory product identifier can no longer be left blank. Which type of Data Quality action is this?
Answer: D
Explanation:
The application change is a preventive Data Quality control because it prevents a known defect from being created in the first place.
A mandatory-field control ensures that records cannot be accepted without the required Product Identifier.
This differs from a detective control, which would identify missing identifiers after records had already entered the system.
Preventive controls are generally preferable where the organization controls the point of data creation and the business rule is sufficiently clear. They reduce downstream cleansing, exception handling, reconciliation, and operational rework.
However, making the field technically mandatory is appropriate only if the business requirement genuinely applies to every relevant record. Governance and stewardship should confirm the rule before implementation.
If certain product categories legitimately lack the identifier, the validation should incorporate those conditions instead of enforcing an overly broad requirement.
The Product Identifier may also link the transaction to governed Product Master Data, making integrity and consistency important alongside completeness.
DAMA's Data Quality framework emphasizes defining rules, detecting defects, implementing improvement, and integrating quality with Governance and Master Data disciplines rather than relying solely on after-the- fact cleansing.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Completeness; Data Quality Improvement Lifecycle; Chapter 10 - Product Master Data; Governance.
NEW QUESTION # 115
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: D
Explanation:
An MDM investment is justified when the business value obtained from centralized coordination of shared data exceeds the additional cost of managing and integrating another controlled representation of that data.
This is the economic trade-off expressed by option C and is also the intended answer in published versions of this DAMA question.
DAMA-DMBOK2 positions Master Data Management as a mechanism for improving consistency and control over enterprise entities such as customers, products, suppliers, employees, and locations. Uncoordinated local copies create redundant maintenance, inconsistent definitions, duplicate records, reconciliation costs, and integration complexity. DAMA sources emphasize that controlling shared master and reference data reduces both cost and risk generated by inconsistencies between systems.
However, introducing an MDM repository also incurs costs: integration, stewardship, matching, survivorship logic, synchronization, governance, maintenance, and potentially another physical copy of shared information.
The business case must therefore demonstrate that improved coordination, quality, reuse, reporting, and operational consistency outweigh those costs.
MDM does not require all contributing systems to be decommissioned, nor does it eliminate Reference Data Management. Similarly, "mining golden records" is not the fundamental economic basis for MDM.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Business Drivers; Shared Data; Central Coordination; Data Integration; Data Quality and Stewardship.
NEW QUESTION # 116
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
Answer: C
Explanation:
An enterprise Master Data Repository serving multiple applications is a classic Hub-and-Spoke integration pattern. In this architecture, shared information is consolidated physically or virtually in a central hub, while participating applications interact with that hub rather than building a separate direct interface to every other application.
DAMA-DMBOK2 explicitly identifies Master Data Management hubs, Data Warehouses, Data Marts, and Operational Data Stores as familiar examples of data hubs. The hub-and-spoke model reduces the proliferation of point-to-point interfaces and can provide a consistent enterprise view of shared data.
This architecture is particularly appropriate for MDM because customer, product, supplier, location, or other master entities often need to be standardized, matched, governed, and redistributed across many systems. The central hub can apply survivorship rules, reference mappings, stewardship decisions, and quality controls before distributing trusted values.
Replication and Change Data Capture are techniques for moving or detecting changes in data, but neither describes the overall interaction topology. Publish-subscribe may operate alongside an MDM hub as a distribution mechanism, but the enterprise repository itself represents the hub within a hub-and-spoke architecture.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Hub-and-Spoke; Integration Interaction Models; Chapter
10 - Master Data Management; Enterprise Master Data Hub; Data Quality Consistency.
NEW QUESTION # 117
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
Answer: B
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 # 118
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