Curious about Actual Dama CDMP (CDMP-RMD) Exam Questions?

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Management of Reference and Master Data aims to mitigate the challenges of disparate data, which typically arise from:

Organic Growth:

Unplanned Expansion: Over time, organizations often develop new systems and applications organically, leading to isolated and redundant data stores.

Inconsistent Data: These disparate systems often result in inconsistent and unreliable data.

Isolated Systems:

Siloed Applications: Independent systems that do not communicate effectively with each other can lead to multiple versions of the same data.

Lack of Integration: Without proper integration, data consistency and quality suffer.

Mergers and Acquisitions:

Combining Systems: Mergers and acquisitions introduce the challenge of integrating different data systems and standards.

Data Redundancy: Newly acquired systems often come with their own data sets, leading to redundancy and conflicts.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Correct : C

Master and Reference Data are forms of Data Architecture. Here's why:

Data Architecture Definition:

Structure and Design: Data architecture involves the structure and design of data systems, including how data is organized, stored, and accessed.

Components: Encompasses various components, including data models, data management processes, and data governance frameworks.

Role of Master and Reference Data:

Core Components: Master and Reference Data are integral components of an organization's data architecture, providing foundational data elements used across multiple systems and processes.

Organization and Integration: They play a critical role in organizing and integrating data, ensuring consistency and accuracy.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Correct : A

MDM (Master Data Management) is characterized by formal management with a high degree of diligence and collaboration. Here's why:

Formal Management:

Structured Processes: MDM involves structured processes for managing master data, including data governance, data quality management, and data stewardship.

Policies and Standards: Establishes and enforces policies and standards to ensure data consistency, accuracy, and integrity.

Collaboration:

Cross-Functional Teams: Requires collaboration across different departments, including IT, business units, and data governance teams.

Stakeholder Involvement: Engages various stakeholders in the data management process, ensuring that master data meets the needs of the entire organization.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Correct : C

Implementing Master Data Management (MDM) involves several challenges, but the disparity between data sources is often the most significant.

Disparity Between Sources:

Different systems and applications often store data in varied formats, structures, and standards, leading to inconsistencies and conflicts.

Data integration from disparate sources requires extensive data cleansing, normalization, and harmonization to create a single, unified view of master data entities.

Data Quality Issues:

Variability in data quality across sources can further complicate the integration process. Inconsistent or inaccurate data must be identified and corrected.

Defining Requirements for Master Data:

While defining requirements is crucial, it is typically a manageable step through collaboration with business and technical stakeholders.

DBA Cooperation:

Getting Database Administrators (DBAs) to share table structures can pose challenges, but it is not as critical as dealing with disparate data sources.

Complex Queries and Indexes:

While important for performance optimization, complex queries and indexing issues are more technical hurdles that can be resolved with appropriate database management practices.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Correct : A

Reference data can be structured in various ways depending on its granularity and complexity.

Simple List:

Reference data can be a simple list when it involves basic, discrete values such as country codes or product categories.

Cross-Reference:

When reference data needs to map values between different systems or standards, it can be structured as cross-references. For example, mapping old product codes to new ones.

Taxonomy:

For more complex hierarchical relationships, reference data can be structured as a taxonomy. This involves categorizing data into parent-child relationships, like an organizational hierarchy or biological classification.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials