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DAMA CDMP-RMD Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.

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DAMA Reference And Master Data Management Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
The format and allowable ranges of Master Data values are dictated by:

A. Processing rules
B. Engagement rules
C. Business rules
D. Database limitations
E. Semantic rules

Answer: C

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.

NEW QUESTION # 20
A catalog where products are organized by category is an example of?

A. A metadata repository
B. A marketing mix
C. A meronomy
D. A taxonomy

Answer: D

Explanation:
A catalog where products are organized by category is an example of a taxonomy. Here's why:
* Definition of Taxonomy:
* Classification System: Taxonomy refers to the practice and science of classification. It involves organizing items into hierarchical categories based on their relationships and similarities.
* Example: In the context of a product catalog, taxonomy is used to classify products into categories and subcategories, making it easier to browse and find specific items.
* Application in Product Catalogs:
* Categorization: Products are grouped into logical categories (e.g., Electronics, Clothing, Home Appliances) and subcategories (e.g., Smartphones, Laptops, Televisions).
* Navigation and Search: Helps users navigate the catalog efficiently and find products quickly by narrowing down categories.
* References:
* Data Management Body of Knowledge (DMBOK), Chapter 9: Data Architecture
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge
* (DMBOK)"

NEW QUESTION # 21
The concept of tracking the number of MDM subject areas and source system attributes Is referred to as:

A. Subject Area and Attribute Scope and Coverage
B. Hub and Spoke
C. Mapping and Integration
D. Publish and Subscribe

Answer: A

Explanation:
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.
References:
* DAMA-DMBOK2 Guide: Chapter 10 - Master and Reference Data Management
* "Master Data Management and Data Governance" by Alex Berson, Larry Dubov

NEW QUESTION # 22
Which is NOT considered a type of Master Data relationship?

A. Fixed-Level Hierarchy
B. Survivorship
C. Ragged-Level Hierarchy
D. Customer Household
E. Grouping based on common criteria

Answer: B

Explanation:
Master Data relationships define how different master data entities are related to each other within an organization. These relationships are crucial for understanding and managing the dataeffectively. The types of master data relationships generally include hierarchies, groupings, and associations that help in organizing and categorizing the data.
* Customer Household:
* This refers to grouping individual customers into a single household entity. It is commonly used in consumer industries to understand the relationships and dynamics within a household.
* Fixed-Level Hierarchy:
* A hierarchy with a predetermined number of levels. Each level has a specific position and relationship to other levels, such as organizational hierarchies or product categorization.
* Ragged-Level Hierarchy:
* Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.
* Grouping based on common criteria:
* This involves creating groups or segments of data based on shared attributes or criteria. For example, grouping products by category or customers by region.
* Survivorship (NOT a relationship):
* Survivorship pertains to the process of determining the most accurate and relevant data when multiple records exist for the same entity. It is a data quality and management process, not a type of relationship.

NEW QUESTION # 23
What role would you expect Data Governance to play in the development of an enterprise wide MDM strategy?

A. Identify different approaches to data processing.
B. Helping the DBAs design efficient database tables
C. Developing xml for data messaging.
D. Identify data sources to be integrated
E. Producing and managing an enterprise conceptual data model to focus and support the MDM strategy

Answer: E

Explanation:
Data Governance plays a pivotal role in the development of an enterprise-wide Master Data Management (MDM) strategy. Here's how:
* Role of Data Governance:
* Policy Development: Data Governance establishes policies and standards for data management to ensure data quality, security, and compliance.
* Data Stewardship: Assigns roles and responsibilities to manage and oversee data assets across the organization.
* MDM Strategy Support:
* Conceptual Data Model:
* Producing and managing an enterprise conceptual data model helps align the organization's data architecture with its business processes.
* It provides a unified view of data entities, their relationships, and how data flows through various systems, ensuring consistency and accuracy.
* Alignment with Business Goals: Ensures that MDM efforts support business objectives by providing a clear framework for data usage and governance.
* References:
* Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

NEW QUESTION # 24
......

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CDMP-RMD Exam Forum - Exam CDMP-RMD Price

DAMA CDMP-RMD Exam Syllabus Topics:

Exam DAMA CDMP-RMD Price & Exam CDMP-RMD Course

DAMA Reference And Master Data Management Exam Sample Questions (Q19-Q24):

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