Questions for the CDMP-RMD were updated on : Nov 21 ,2025
Depending on the granularity and complexity of what the Reference Data represents. it may be
structured as a simple list, a cross-reference or a taxonomy.
A
Explanation:
Reference data can be structured in various ways depending on its granularity and complexity.
Simple List:
Reference data can be a simple list when it involves basic, discrete values such as country codes or
product categories.
Cross-Reference:
When reference data needs to map values between different systems or standards, it can be
structured as cross-references. For example, mapping old product codes to new ones.
Taxonomy:
For more complex hierarchical relationships, reference data can be structured as a taxonomy. This
involves categorizing data into parent-child relationships, like an organizational hierarchy or
biological classification.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
The format and allowable ranges of Master Data values are dictated by:
A
Explanation:
The format and allowable ranges of Master Data values are primarily dictated by business rules.
Business Rules:
Business rules define the constraints, formats, and permissible values for master data based on the
organization’s operational and regulatory requirements.
These rules ensure that data conforms to the standards and requirements necessary for effective
business operations.
Semantic Rules:
These rules pertain to the meaning and context of the data but do not directly dictate formats and
ranges.
Processing Rules:
These rules focus on how data is processed but not on the allowable values or formats.
Engagement Rules:
These rules govern interactions and workflows rather than data formats and ranges.
Database Limitations:
While database limitations can impose constraints, they are typically secondary to the business rules
that drive data requirements.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
The biggest challenge to implementing Master Data Management will be:
C
Explanation:
Implementing Master Data Management (MDM) involves several challenges, but the disparity
between data sources is often the most significant.
Disparity Between Sources:
Different systems and applications often store data in varied formats, structures, and standards,
leading to inconsistencies and conflicts.
Data integration from disparate sources requires extensive data cleansing, normalization, and
harmonization to create a single, unified view of master data entities.
Data Quality Issues:
Variability in data quality across sources can further complicate the integration process. Inconsistent
or inaccurate data must be identified and corrected.
Defining Requirements for Master Data:
While defining requirements is crucial, it is typically a manageable step through collaboration with
business and technical stakeholders.
DBA Cooperation:
Getting Database Administrators (DBAs) to share table structures can pose challenges, but it is not as
critical as dealing with disparate data sources.
Complex Queries and Indexes:
While important for performance optimization, complex queries and indexing issues are more
technical hurdles that can be resolved with appropriate database management practices.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Which of the following is true about MDM?
A
Explanation:
MDM (Master Data Management) is characterized by formal management with a high degree of
diligence and collaboration. Here’s why:
Formal Management:
Structured Processes: MDM involves structured processes for managing master data, including data
governance, data quality management, and data stewardship.
Policies and Standards: Establishes and enforces policies and standards to ensure data consistency,
accuracy, and integrity.
Collaboration:
Cross-Functional Teams: Requires collaboration across different departments, including IT, business
units, and data governance teams.
Stakeholder Involvement: Engages various stakeholders in the data management process, ensuring
that master data meets the needs of the entire organization.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Master and Reference Data are forms of:
C
Explanation:
Master and Reference Data are forms of Data Architecture. Here’s why:
Data Architecture Definition:
Structure and Design: Data architecture involves the structure and design of data systems, including
how data is organized, stored, and accessed.
Components: Encompasses various components, including data models, data management
processes, and data governance frameworks.
Role of Master and Reference Data:
Core Components: Master and Reference Data are integral components of an organization’s data
architecture, providing foundational data elements used across multiple systems and processes.
Organization and Integration: They play a critical role in organizing and integrating data, ensuring
consistency and accuracy.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
What characteristics does Reference data have that distinguish it from Master Data?
C
Explanation:
Reference data and master data are distinct in several key characteristics. Here’s a detailed
explanation:
Reference Data Characteristics:
Stability: Reference data is generally less volatile and changes less frequently compared to master
data.
Complexity: It is less complex, often consisting of simple lists or codes (e.g., country codes, currency
codes).
Size: Reference data sets are typically smaller in size than master data sets.
Master Data Characteristics:
Volatility: Master data can be more volatile, with frequent updates (e.g., customer addresses,
product details).
Complexity: More complex structures and relationships, involving multiple attributes and entities.
Size: Larger in size due to the detailed information and numerous entities it encompasses.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Every process within a MDM framework includes:
D
Explanation:
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.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Which of the following is NOT an example of Master Data?
C
Explanation:
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.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
All organizations have master data even if it is not labelled Master Data.
A
Explanation:
All organizations possess master data, even if it is not explicitly labeled as such. Here’s why:
Definition of Master Data:
Core Business Entities: Master data refers to the critical entities around which business transactions
are conducted, such as customers, products, suppliers, and accounts.
Business Operations: Every organization maintains records of these entities to support business
operations, decision-making, and reporting.
Implicit Existence:
Unlabeled Data: Organizations may not explicitly label this data as “Master Data,” but it exists within
various systems, databases, and spreadsheets.
Examples: Customer lists, product catalogs, employee records, and financial accounts.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Is there a standard tor defining and exchanging Master Data?
A
Explanation:
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.
Reference:
ISO 22745 Documentation
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Master Data and metadata ran both he used to aggregate dat
a. Master Data require* that the organization:
B
Explanation:
Master data and metadata are both used to aggregate data, but master data requires that the
organization identifies or develops a trusted version of truth for each of its entities.
Trusted Version of Truth:
For effective master data management, an organization must establish a single, trusted version of
truth for each master data entity (e.g., customer, product).
This involves harmonizing data from various sources, resolving duplicates, and ensuring consistency
and accuracy.
Master Data:
Master data includes critical business information that provides context for business transactions and
analysis. It must be consistent, accurate, and up-to-date to support operational and analytical
processes.
Other Options:
Transaction Activity Data: Important for operational processes but not the focus for creating master
data.
One Set of Data as Source and Target: Not sufficient for managing master data.
Specific Application Solutions: While useful, they do not ensure the creation of a trusted version of
truth for master data.
Transaction Audit Data: Important for auditing but not central to master data creation.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
International Classification of Diseases (ICD) codes are an example of:
A
Explanation:
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.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
WHO ICD Documentation
An authoritative system where data consumers can obtain reliable data as an alternative to the
system of record to support transactions and analysis is known as:
D
Explanation:
An authoritative system where data consumers can obtain reliable data as an alternative to the
system of record is known as a "Trusted System."
System of Record:
The system of record (SOR) is the authoritative data source for a particular data element or dataset. It
ensures data integrity, accuracy, and consistency.
Trusted System:
A trusted system provides reliable data that consumers can use for transactions and analysis. It acts
as a reference point and may serve as an alternative to the system of record.
It ensures that users have access to high-quality, consistent, and trustworthy data, which is essential
for decision-making and operational processes.
Other Options:
System of Reference: Generally refers to a system used for lookup and reference purposes but not
necessarily authoritative for transactions.
System of Origin: The original source of data before it is integrated into other systems.
Source System: Any system that contributes data to an enterprise system but is not specifically a
trusted or authoritative source.
System of Use: The system where data is actively used and consumed for various business processes.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Reference and Master data ran be stored in separate repositories:
A
Explanation:
Reference data and master data serve different purposes within an organization, and storing them in
separate repositories can be beneficial for managing them effectively.
Reference Data:
Reference data is used to classify or categorize other data. Examples include code tables,
taxonomies, and standard lists like country codes or industry classifications.
It is often less volatile and has a higher degree of standardization.
Master Data:
Master data refers to the core business entities that are essential for operations, such as customers,
products, employees, and suppliers.
It is often more dynamic and requires frequent updates to ensure accuracy and consistency across
systems.
Separate Repositories:
Storing reference and master data in separate repositories allows for tailored management
strategies, governance, and security measures suited to their specific needs.
This approach can improve performance, data quality, and accessibility by reducing complexity and
focusing resources on maintaining each type of data appropriately.
Reference:
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Master Data Curation is used for improving the overall quality of the data throughout the business by
doing the following:
E
Explanation:
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.
Reference:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"