QUESTION: 49

MOM is most accurately and comprehensively defined in which of the following definitions?

Processes that maintain master data
The integration of systems of record that can be leveraged by a governance program
The creation of a single instance of an attribute across the enterprise as the version of the truth
A technology foundation for the management of key business entities
Governed processes enabled by people and technologies providing Master Data that is understood, trusted, controlled, and fit-for-purpose

Answer(s): E

Explanation:

Master Data Management (MDM) involves various processes and technologies to ensure that master data is accurate, consistent, and trustworthy. The most comprehensive definition of MDM captures its multi-faceted nature, encompassing governance, technology, and organizational roles.
Governed Processes:
MDM involves establishing governance processes to define policies, standards, and procedures for managing master data.
These processes ensure that data is handled consistently and according to defined rules.
Role of People and Technologies:
Effective MDM requires the involvement of people, including data stewards, data owners, and governance committees, who are responsible for overseeing and managing master data. Technologies, such as MDM software and tools, facilitate the implementation of governance processes, data integration, data quality management, and synchronization.
Key Objectives:
Master data should be understood by stakeholders, ensuring clarity and common understanding of data definitions and attributes.
Trust in master data is achieved through rigorous data quality and governance practices. Data should be controlled, meaning that access, usage, and changes to the data are managed and monitored.
Master data must be fit-for-purpose, meeting the specific needs and requirements of the organization's business processes.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework CDMP (Certified Data Management Professional) Exam Study Materials

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QUESTION: 50

What is a Canonical Data Model?

Used for data replication
Data that is not used frequently is moved to the Canonical model
A common data model which standardizes the format in which data will be shared
An alternate data structure which is less costly to the organization
Used in a transactional operating environment

Answer(s): C

Explanation:

A Canonical Data Model (CDM) is a design pattern used in data integration and exchange to create a standardized format for data that will be shared across different systems.
Canonical Data Model Definition:
A CDM provides a common, consistent, and agreed-upon representation of data across different systems within an organization.

It serves as an intermediary format to simplify data exchange and integration.
Standardization:
The primary purpose of a CDM is to standardize the format of data. This ensures that data from different sources can be integrated, understood, and used without needing extensive transformation or reformatting.
It addresses the problem of data heterogeneity, where different systems may have their own data formats and structures.
Data Integration:
By using a CDM, organizations can facilitate easier and more efficient data integration processes. Data from various systems can be mapped to the canonical model, enabling seamless data exchange and interoperability.
It simplifies the maintenance and management of data integration solutions by providing a single, unified data model.
Use Cases:
CDMs are commonly used in enterprise application integration (EAI), service-oriented architectures (SOA), and other environments where data needs to be exchanged between multiple systems.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework CDMP (Certified Data Management Professional) Exam Study Materials

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QUESTION: 51

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

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

Answer(s): C

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.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

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QUESTION: 52

Key processing steps for successful MDM include the following steps with the exception of which processing step?

Data Indexing
Data Acquisition
Data Sharing & Stewardship
Entity Resolution
Data Model Management

Answer(s): A

Explanation:

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.

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)"

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Free CDMP-RMD Exam Braindumps (page: 13)

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