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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 Mar. 22, 2025 (see below)
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)'
What characteristics does Reference data have that distinguish it from Master Data?
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.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
The format and allowable ranges of Master Data values are dictated by:
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.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Which of the following is NOT an example of Master Data?
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)'
Every process within a MDM framework includes:
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.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
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