Important Dama CDMP-RMD Exam Questions

Answer: D

Explanation:

Every process within an MDM framework includes a degree of governance. Here's why:

Governance Definition:

Policies and Standards: Governance involves the establishment of policies, standards, and procedures to ensure data quality, consistency, and compliance.

Oversight: Provides oversight and accountability for data management practices.

MDM Processes:

Inherent Governance: All MDM processes, from data integration to data quality management, incorporate governance to ensure the integrity and reliability of master data.

Data Stewardship: Involves data stewards who oversee data governance activities, ensuring adherence to established standards and policies.

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

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

Answer: D

Explanation:

Definitions and Context:

Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization's official shared master data assets.

Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.

Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization.

Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.

Gartner Research, 'The Impact of Master Data Management on Data Quality.'

Answer: D

Explanation:

MDM matching algorithms benefit from various data characteristics but do not benefit from 'Structural heterogeneity of data elements.'

Matching Algorithms: These are used in MDM to identify and link data records that refer to the same entity across different systems.

Data Characteristics:

Distinctiveness: Helps in accurately matching records.

Common Data Points: Aids in the comparison process.

Comparability: Facilitates effective matching.

Validity: Ensures the data is accurate and reliable.

Structural Heterogeneity: Different structures can complicate the matching process, making it harder to align data.

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

CDMP Study Guide

Answer: C

Explanation:

Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.

Government Sources:

While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.

Drop-Down Menus:

Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.

Data Governance and Stewardship:

Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.

Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.

Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.

Standard Reference Data (ISO):

Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.

External Data Providers:

External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer: A

Explanation:

The hub-and-spoke model is most often used for Master Data Management because it provides a central hub where master data is maintained, while the spokes represent different systems or applications that interact with the hub. This model allows for efficient management, synchronization, and distribution of master data across the enterprise, ensuring consistency and quality.

DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball and Margy Ross.