QUESTION: 45

The Data Architecture design of an MDM solution must resolve where to leverage what type of relationships?

Traceable relationships and/or lineage relationships
Data Acquisition relationships
Affiliation relationships and/or parent-child relationships
Hub and spoke relationships
Ontology relationships and/or epistemology relationships

Answer(s): C

Explanation:

Data Architecture in MDM Solutions: The design of a Master Data Management (MDM) solution involves defining and managing relationships between data entities.
Types of Relationships:
Traceable relationships and/or lineage relationships: These are important for understanding data provenance and transformations but are more relevant to data governance and data lineage tracking. Data Acquisition relationships: These pertain to how data is sourced and collected, rather than how master data entities are related.
Affiliation relationships and/or parent-child relationships: These are crucial in MDM as they define how entities are related in hierarchical and associative contexts, such as customer relationships, organizational hierarchies, and product categorizations. Hub and spoke relationships: This refers to the architecture model for MDM systems rather than the type of data relationship.
Ontology relationships and/or epistemology relationships: These are more abstract and pertain to the nature and categorization of knowledge, not specifically to the functional relationships in MDM. Conclusion: The correct answer is "Affiliation relationships and/or parent-child relationships" as these are essential for defining and managing master data relationships in an MDM solution.

Reference:

DMBOK Guide, sections on Data Architecture and Master Data Management.
CDMP Examination Study Materials.

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QUESTION: 46

What item listed will be determined by Reference & Master Data governance processes?

Total cost of ownership
Service level agreements
None of these
Data sharing volume and usage
Data change activity

Answer(s): E

Explanation:

Reference and Master Data Management (RMDM) governance processes are designed to manage and ensure the accuracy, consistency, and quality of critical data assets across an organization. These processes focus on defining, maintaining, and governing the shared data entities and attributes that are essential for various business processes. One of the key aspects governed by RMDM is "Data change activity."
Reference and Master Data Definition:
Reference data is a subset of master data used to classify or categorize other data within an organization. It typically includes codes and descriptions. Master data refers to the critical business information regarding the core entities around which business is conducted, such as customers, products, employees, and suppliers.
Data Change Activity:

This involves tracking and managing the changes made to master and reference data over time. The governance processes ensure that any changes to this data are properly authorized, recorded, and communicated to relevant stakeholders.
Managing data change activity includes monitoring modifications, updates, additions, and deletions of reference and master data.
Importance in Governance:
Effective governance of data change activity ensures that the integrity and quality of master data are maintained. It prevents unauthorized changes that could lead to data inconsistencies and inaccuracies.
It supports audit trails and compliance with regulatory requirements by providing transparency and accountability for data changes.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework CDMP (Certified Data Management Professional) Exam Study Materials

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QUESTION: 47

Can the kinds of information treated as master data vary from one industry to another and even from one company to another within the same industry?

Yes. each industry and/or company has their own core master data
No. master data for an industry is always standardized
No. master data is always the same kind of information

Answer(s): A

Explanation:

Master data refers to the critical data that is essential to the operations of a business. It typically includes entities such as customers, products, employees, suppliers, and other key business entities. The kinds of information treated as master data can vary widely between industries and even between companies within the same industry.
Industry-Specific Master Data:
Different industries have distinct core data entities critical to their operations. For example, in the healthcare industry, patient and provider data are crucial, whereas, in the retail industry, product and customer data are paramount.
Companies in regulated industries may have specific master data requirements mandated by regulatory bodies.
Company-Specific Master Data:
Within the same industry, different companies may prioritize different sets of master data based on their unique business processes, strategies, and operational needs. Organizational size, structure, and business model can influence what is considered master data.
Customization and Flexibility:
Master data management (MDM) systems and practices are designed to be flexible to accommodate the unique needs of different organizations.
Customizing MDM allows companies to manage and maintain the integrity of the specific data entities that are critical to their success.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

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QUESTION: 48

What MDM style allows data to be authored anywhere?

Consolidation
Centralized style
Persistent
Registry style
Coexistence

Answer(s): E

Explanation:

Master Data Management (MDM) styles define how and where master data is managed within an organization. One of these styles is the "Coexistence" style, which allows data to be authored and maintained across different systems while ensuring consistency and synchronization.
Coexistence Style:
The coexistence style of MDM allows master data to be created and updated in multiple locations or systems within an organization.
It supports the integration and synchronization of data across these systems to maintain a single, consistent view of the data.
Key Features:
Data Authoring: Data can be authored and updated in various operational systems rather than being confined to a central hub.
Synchronization: Changes made in one system are synchronized across other systems to ensure data consistency and accuracy.
Flexibility: This style provides flexibility to organizations with complex and distributed IT environments, where different departments or units may use different systems.
Benefits:
Enhances data availability and accessibility across the organization. Supports operational efficiency by allowing data updates to occur where the data is used. Reduces the risk of data silos and inconsistencies by ensuring data synchronization.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

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QUESTION: 48

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