DAMA Reference And Master Data Management Exam CDMP-RMD Exam Dumps: Updated Questions & Answers (June 2025)

Every process within an MDM framework includes a degree of governance. Here’s why:

Governance Definition:

Policies and Standards: Governance involves the establishment of policies, standards, and procedures to ensure data quality, consistency, and compliance.

Oversight: Provides oversight and accountability for data management practices.

MDM Processes:

Inherent Governance: All MDM processes, from data integration to data quality management, incorporate governance to ensure the integrity and reliability of master data.

Data Stewardship: Involves data stewards who oversee data governance activities, ensuring adherence to established standards and policies.

References:

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

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

Sharing reference and master data across an enterprise requires effective collaboration and communication among various stakeholders within the organization.

Staging Area:

A staging area can be used for intermediate data storage during processing but is not a requirement for sharing data.

Maintaining and Storing History Records:

Historical records are important for auditing and tracking changes but do not directly facilitate the sharing of current reference and master data.

Collaboration Between Multiple Parties Internal to the Organization:

Effective sharing of master and reference data requires collaboration among different departments and stakeholders to ensure data consistency, quality, and governance.

This includes establishing clear communication channels, defining roles and responsibilities, and ensuring alignment on data standards and practices.

Identification of Business Key and Surrogate Keys:

Keys are important for data integration and linking but do not by themselves ensure effective sharing of data.

Creation of Foreign Keys to Support Dimensions:

Foreign keys are used in relational databases to link tables but are not specifically required for the sharing of master data.

In any Master Data Management (MDM) effort, the most time and energy are typically spent on vetting business entities and data attributes through the Data Governance process. This step ensures that the data is accurate, consistent, and adheres to defined standards and policies. Itinvolves significant collaboration and decision-making among stakeholders to validate and approve the data elements to be managed.

References:

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.

"Master Data Management and Data Governance" by Alex Berson and Larry Dubov.

Measuring metadata documentation quality involves several metrics that collectively provide a comprehensive view of the quality and effectiveness of metadata management practices.

Random Survey based on Enterprise Definition of Quality:

Conducting surveys among data users to gather feedback on the perceived quality of metadata documentation. This helps in understanding user satisfaction and identifying areas for improvement.

Currency of Metadata in the Repository:

Ensuring that metadata is up-to-date and accurately reflects the current state of the data. This is crucial for maintaining the relevance and usefulness of metadata.

Collision Logic on Two Sources Measuring How Much They Match:

Comparing metadata from different sources to identify discrepancies and ensure consistency. This metric helps in assessing the alignment and accuracy of metadata across systems.

Percentage of Attributes that have Definitions:

Measuring the completeness of metadata by checking the percentage of attributes that have well-defined descriptions. This ensures that all data elements are clearly documented and understood.

A key point of a Master Data Management (MDM) program is that it must adapt and evolve over time. The statement that an MDM program "can be effectively created and managed long-term using the same methodology" is not correct. MDM programs must continually evolve to address new data sources, changing business requirements, and advancements in technology. As data inventory grows and the data landscape changes, MDM methodologies and strategies need to be reassessed and updated to remain effective. This adaptability is crucial for maintaining data quality and relevance.

References:

DAMA-DMBOK2 Guide: Chapter 10 – Master and Reference Data Management

"Master Data Management and Data Governance" by Alex Berson, Larry Dubov

MDM (Master Data Management) is a lifecycle management process that includes various activities to ensure the quality, consistency, and accessibility of master data across an organization. These activities include:

Provisioning of Access: Ensuring that trusted master data is accessible across applications through various methods such as direct reads, data services, or replication feeds.

Identifying Multiple Instances: Detecting and managing multiple representations of the same entity within and across data sources. This involves creating and maintaining identifiers and cross-references for integration.

Enforcing Use of Master Data: Ensuring that the organization consistently uses master data values in processes and applications.

Resolving Improper Matches: Identifying and resolving improperly matched or merged data instances to maintain data integrity.

The activity of "Ensuring effective and efficient retrieval and use of data and information by ETL logic" (C) is not specific to MDM. While ETL (Extract, Transform, Load) processes are crucial for data integration and warehousing, they are not a core activity unique to the MDM lifecycle.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

"Master Data Management and Data Governance" by Alex Berson and Larry Dubov.

Master Data relationships define how different master data entities are related to each other within an organization. These relationships are crucial for understanding and managing the dataeffectively. The types of master data relationships generally include hierarchies, groupings, and associations that help in organizing and categorizing the data.

Customer Household:

This refers to grouping individual customers into a single household entity. It is commonly used in consumer industries to understand the relationships and dynamics within a household.

Fixed-Level Hierarchy:

A hierarchy with a predetermined number of levels. Each level has a specific position and relationship to other levels, such as organizational hierarchies or product categorization.

Ragged-Level Hierarchy:

Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.

Grouping based on common criteria:

This involves creating groups or segments of data based on shared attributes or criteria. For example, grouping products by category or customers by region.

Survivorship (NOT a relationship):

Survivorship pertains to the process of determining the most accurate and relevant data when multiple records exist for the same entity. It is a data quality and management process, not a type of relationship.

The governance scope of an MDM program should be comprehensive, encompassing all aspects and stakeholders involved in the management and usage of master data.

Internal Data Subscribers to the Master Data:

Internal users and systems that consume master data must be governed to ensure they have access to accurate and consistent data.

Master Data Authored in the MDM User Interface:

Governance should include the processes and controls around how master data is created and maintained through the MDM interface.

External Third Parties Using Master Data:

External parties that use or access master data must be governed to ensure data security, privacy, and compliance with regulations.

Data Sources of the Master Data:

The sources from which master data is derived must be governed to ensure data quality, integrity, and consistency.

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.

Master Data Curation is a process aimed at improving the overall quality of data throughout the business. Here’s how:

Data Quality Improvement:

De-duplication: The process involves identifying and eliminating duplicate records to ensure a single, accurate version of each data entity.

Data Cleaning: Removes inaccuracies and inconsistencies, enhancing the reliability of the data.

Benefits of De-duplication:

Accuracy: Ensures that each entity (e.g., customer, product) is represented only once, improving data accuracy and reducing redundancy.

Operational Efficiency: Streamlines operations by eliminating duplicate records that can cause confusion and errors in business processes.

References:

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

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