CDMP-RMD Exam Questions and Answers

QUESTION: 16

ISO 8000 is a Master Data international standard tor what purpose?

Provides a standard format for defining a model for a data dictionary
Provide guidance only to the Buy side of the supply chain
To replace the ISO 9000 standard
Define and measure data quality
Defines a format to exchange data between parties

Answer(s): D

Explanation:

ISO 8000 is an international standard focused on data quality and information exchange. Its primary purpose is to define and measure the quality of data, ensuring that it meets the requirements for completeness, accuracy, and consistency. The standard provides guidelines for data quality management, including requirements for data governance, data quality metrics, and procedures for improving data quality over time. ISO 8000 is not meant to replace ISO 9000, which is focused on quality management systems, but to complement it by addressing data quality specifically.

Reference:

ISO 8000: Overview and Benefits of ISO 8000, International Organization for Standardization (ISO) DAMA-DMBOK2 Guide: Chapter 12 Data Quality Management

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QUESTION: 17

An organization chart where a high level manager has department managers with staff and non- managers without staff as direct reports would best be maintained in which of the following?

A fixed level hierarchy
A ragged hierarchy
A reference file
A taxonomy
A data dictionary

Answer(s): B

Explanation:

A ragged hierarchy is an organizational structure where different branches of the hierarchy can have varying levels of depth. This means that not all branches have the same number of levels. In the given scenario, where a high-level manager has department managers with staff and non-managers without staff as direct reports, the hierarchy does not have a uniform depth across all branches. This kind of structure is best represented and maintained as a ragged hierarchy, which allows for flexibility in representing varying levels of managerial relationships and reporting structures.

Reference:

DAMA-DMBOK2 Guide: Chapter 7 Data Architecture Management "Master Data Management and Data Governance" by Alex Berson, Larry Dubov

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QUESTION: 18

Matching or candidate identification is the process called similarity analysis. One approach is called deterministic which relies on:

Statistical techniques for assessing the probability that any pair of records represents the same entity
Taking data samples and looking at results for a subset of the records
Being able to determine the similarity between two data models
Algorithms for parsing and standardization and on defined patterns and rules for determining similarity
Finding two references that are linked with a single entity

Answer(s): D

Explanation:

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.

Reference:

DAMA-DMBOK2 Guide: Chapter 10 Master and Reference Data Management "Entity Resolution and Information Quality" by John R. Talburt

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QUESTION: 19

What statement is NOT correct as a key point of a MDM program?

Must continually prove and promote its accomplishments and benefits
Program funding requirements typically grow over time as the data inventory grows
Has an indefinite life span
Should be in scope for Big Data and loT initiatives
Can be effectively created and managed long-term using the same methodology

Answer(s): E

Explanation:

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.

Reference:

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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QUESTION: 20

Taxonomic Reference Data enable which of the following?

Content classification and multi-level navigation to support Business Intelligence
The use of canonical models
Having data models physically instantiated in multiple platforms
Key processing steps for MDMs
Source systems named differently than target systems

Answer(s): A

Explanation:

Taxonomic reference data involves categorizing and organizing data to enable structured access and retrieval. It facilitates content classification, allowing for efficient multi-level navigation, which is essential for Business Intelligence (BI) activities. By organizing data into a taxonomy, users can easily locate and analyze information, supporting better decision-making processes in BI.

Reference:

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

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Dama CDMP-RMD Exam (page: 4) Dama Reference And Master Data Management Updated on: 25-Dec-2025

QUESTION: 16

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QUESTION: 17

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QUESTION: 18

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QUESTION: 19

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QUESTION: 20

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Dama CDMP-RMD Exam (page: 4)
Dama Reference And Master Data Management
Updated on: 25-Dec-2025