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Dama CDMP-RMD Exam Questions
- Topic 1: Fundamentals of Reference and Master Data: The fundamental ideas of reference and master data are covered in this section, along with their definitions, distinctions, and significance in data management.
- Topic 2: Data Governance: The function of data governance in reference and master data management is examined in this section. It addresses policies, accountability, and data ownership.
- Topic 3: Data Architecture: The design and implementation of data architecture for reference and master data are covered in detail in this topic. It addresses concepts including integration, standardization, and data modeling.
- Topic 4: Data Stewardship: The management of reference and master data by data stewards is the focal point of this topic. It addresses concepts such as accuracy, consistency, and quality of data.
- Topic 5: Data Quality Management: The methods and procedures for guaranteeing the quality of reference and master data are examined in this topic. Moreover, it focuses on validation, monitoring, and data cleansing.
- Topic 6: Master Data Management (MDM) Lifecycle: The phases of the MDM lifecycle such as planning, design, implementation, and continuous operation are covered in this section.
- Topic 7: Reference Data Management (RDM) Lifecycle: The lifecycle of reference data management is covered in this topic.
- Topic 8: MDM and RDM Tools and Technologies: The technologies and tools available for managing reference and master data are discussed in this section.
Free Dama CDMP-RMD Exam Actual Questions
Note: Premium Questions for CDMP-RMD were last updated On Apr. 10, 2025 (see below)
Is there a standard tor defining and exchanging Master Data?
ISO 22745 is an international standard for defining and exchanging master data.
ISO 22745:
This standard specifies the requirements for the exchange of master data, particularly in industrial and manufacturing contexts.
It includes guidelines for the structured exchange of information, ensuring that data can be shared and understood across different systems and organizations.
Standards for Master Data:
Standards like ISO 22745 help ensure consistency, interoperability, and data quality across different platforms and entities.
They provide a common framework for defining and exchanging master data, facilitating smoother data integration and management processes.
Other Options:
ETL: Refers to the process of Extract, Transform, Load, used in data integration but not a standard for defining master data.
Corporation-specific Methods: Many organizations may have their own methods, but standardized frameworks like ISO 22745 provide a common foundation.
No Standards: While not all organizations use master data, standards do exist for those that do.
ISO 22745 Documentation
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Key processing steps for successful MDM include the following steps with the exception of which processing step?
Key processing steps for successful MDM typically include:
Data Acquisition: The process of gathering and importing data from various sources.
Data Sharing & Stewardship: Involves ensuring data is shared appropriately across the organization and that data stewards manage data quality and integrity.
Entity Resolution: Identifying and linking data records that refer to the same entity across different data sources.
Data Model Management: Creating and maintaining data models that define how data is structured and related within the MDM system.
Excluded Step - Data Indexing: While indexing is a critical database performance optimization technique, it is not a primary processing step specific to MDM. MDM focuses on consolidating, managing, and ensuring the quality of master data rather than indexing, which is more about search optimization within databases.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
International Classification of Diseases (ICD) codes are an example of:
International Classification of Diseases (ICD) codes are a type of industry reference data.
ICD Codes:
Developed by the World Health Organization (WHO), ICD codes are used globally to classify and code all diagnoses, symptoms, and procedures recorded in conjunction with hospital care.
They are essential for health care management, epidemiology, and clinical purposes.
Industry Reference Data:
Industry reference data pertains to standardized data used within a particular industry to ensure consistency, accuracy, and interoperability.
ICD codes fall into this category as they are standardized across the healthcare industry, facilitating uniformity in data reporting and analysis.
Other Options:
Geographic Reference Data: Includes data like country codes, region codes, and GPS coordinates.
Computational Reference Data: Used in computational processes and algorithms.
Internal Reference Data: Data used internally within an organization that is not standardized across industries.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
WHO ICD Documentation
Why would a company not develop Master Data?
Several factors can deter a company from developing a master data program, including the perceived value, commitment level, disruption, and data quality priorities.
Fail to See Value in Integrating Their Data:
If a company does not recognize the benefits of integrating and managing master data, it may not invest in an MDM program.
Lack of Commitment:
Developing an effective MDM program requires long-term commitment from leadership and stakeholders. Without this commitment, the program is unlikely to succeed.
The Process is Too Disruptive:
Implementing an MDM program can be disruptive to existing processes and systems. The perceived disruption can deter companies from pursuing it.
Data Quality is Not a Priority:
If a company does not prioritize data quality, it may not see the need for a robust MDM program. Poor data quality can undermine the effectiveness of business processes and decision-making.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
The concept of tracking the number of MDM subject areas and source system attributes Is referred to as:
Tracking the number of MDM subject areas and source system attributes refers to defining the scope and coverage of the subject areas and attributes involved in an MDM initiative. This process includes identifying all the data entities (subject areas) and the specific attributes (data elements) within those entities that need to be managed across the organization. By establishing a clear scope and coverage, organizations can ensure that all relevant data is accounted for and appropriately managed.
DAMA-DMBOK2 Guide: Chapter 10 -- Master and Reference Data Management
'Master Data Management and Data Governance' by Alex Berson, Larry Dubov
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