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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 Jul. 18, 2025 (see below)
Which of the following is NOT a possible outcome of a probabilistic matching algorithm?
Understanding Probabilistic Matching: Probabilistic matching algorithms are used in data matching processes to compare records and determine if they refer to the same entity. These algorithms use statistical techniques to calculate the likelihood of matches.
Possible Outcomes of Probabilistic Matching:
Likely Match: The algorithm determines that the records are probably referring to the same entity based on calculated probabilities.
Non-match: The algorithm determines that the records do not refer to the same entity.
Match: The algorithm determines with high confidence that the records refer to the same entity.
Non-Standard Outcome (D): The term 'Underminable match' is not a standard term used in probabilistic matching outcomes. Typically, if the algorithm cannot determine a match or non-match, it might categorize it as 'possible match' or leave it undecided but not as 'underminable.'
Conclusion: The term 'Underminable match' does not fit into the standard categories of probabilistic matching outcomes.
DMBOK Guide, specifically the sections on Data Quality and Data Matching Techniques.
Industry standards and documentation on probabilistic data matching algorithms.
Matching or candidate identification is the process called similarity analysis. One approach is called deterministic which relies on:
Deterministic matching, also known as exact matching, relies on predefined rules and algorithms to parse and standardize data, ensuring that records are compared based on exact or standardized values. This approach uses defined patterns and rules to determine whether two records represent the same entity by matching key attributes exactly. Deterministic matching is precise and unambiguous, making it a common approach for high-certainty matching tasks, although it can be less flexible than probabilistic methods that allow for variations in data.
DAMA-DMBOK2 Guide: Chapter 10 -- Master and Reference Data Management
'Entity Resolution and Information Quality' by John R. Talburt
Managing master data elements can be performed at which of the following points?
Managing master data elements can be performed at multiple levels within an organization. This includes third-party providers such as Dun & Bradstreet (D&B) which can supply enriched and standardized master data. At the enterprise level, organizations manage master data centrally to ensure consistency and quality across all systems and processes. Within application suites such as ERP (Enterprise Resource Planning) systems, master data management ensures that data is consistent and accurate within and across different applications. Therefore, master data elements can be managed at all these points.
DAMA-DMBOK2 Guide: Chapter 10 -- Master and Reference Data Management
'The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling' by Ralph Kimball
What statement is NOT correct as a key point of a MDM program?
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.
DAMA-DMBOK2 Guide: Chapter 10 -- Master and Reference Data Management
'Master Data Management and Data Governance' by Alex Berson, Larry Dubov
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
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