Dama CDMP-RMD Reference And Master Data Management Exam Practice Test

Answer : C

Planned control activities are not considered master data. Here's why:

Master Data Examples:

Categories and Lists: Master data typically includes lists and categorizations that are used repeatedly across multiple business processes and systems.

Examples: Product categories, account codes, country codes, and currency codes, which are relatively stable and broadly used.

Planned Control Activities:

Process-Specific: Planned control activities pertain to specific actions and checks within business processes, often linked to operational or transactional data.

Not Repeated Data: They are not reused or referenced as a stable entity across different systems.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Answer : D

Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.

Data about Business Entities:

Master data typically includes key entities such as customers, products, suppliers, employees, and locations.

These entities are fundamental to business operations and provide the necessary context for transactions and analysis.

Providing Context for Business Transactions:

Master data provides the foundational information required to conduct business transactions.

For example, customer master data is used in sales transactions, while product master data is used in inventory management.

Supporting Business Analysis:

Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.

It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.

Other Options:

A: Master data and reference data are distinct; reference data is used to categorize master data.

B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.

C: Provides visibility but also context for transactions and analysis.

E: Master data is about business entities, not technical entities.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : C

Ownership is a crucial principle in managing Reference and Master Data. Here's an in-depth look at why:

Organizational Ownership:

Unified Responsibility: Reference and Master Data are assets that span across various functions and departments within an organization.

Consistency and Accuracy: Ensuring that data ownership is attributed to the organization prevents silos and ensures data is consistently accurate and available across all departments.

Data Governance: Proper governance frameworks ensure that data is managed in a way that meets the organization's needs and complies with relevant regulations and standards.

Avoiding Departmental Silos:

Cross-functional Use: Different departments use and rely on Reference and Master Data, so ownership by a single department can lead to conflicts and inconsistencies.

Holistic Management: Centralized ownership enables holistic data management practices, enhancing data quality and usability across the organization.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Answer : C

Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.

Government Sources:

While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.

Drop-Down Menus:

Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.

Data Governance and Stewardship:

Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.

Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.

Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.

Standard Reference Data (ISO):

Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.

External Data Providers:

External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : E

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : A

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.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : E

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.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials