Achieve an Excellent Score in Your DAMA CDMP-RMD Exam with Exam4Labs

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DAMA CDMP-RMD Exam Syllabus Topics:

Topic	Details
Topic 1	Data Governance: The function of data governance in reference and master data management is examined in this section. It addresses policies, accountability, and data ownership.
Topic 2	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 3	Reference Data Management (RDM) Lifecycle: The lifecycle of reference data management is covered in this topic.
Topic 4	Master Data Management (MDM) Lifecycle: The phases of the MDM lifecycle such as planning, design, implementation, and continuous operation are covered in this section.
Topic 5	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 6	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.

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DAMA Reference And Master Data Management Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
The MDM process step responsible for determining whether two references to real world objects refer to the same object or different objects is known as:

A. Data Acquisition
B. Data Sharing & Stewardship
C. Data Model Management
D. Data Validation. Standardization, and Enrichment
E. Entity Resolution

Answer: E

Explanation:
Entity resolution is a critical step in the MDM process that identifies whether different data records refer to the same real-world entity. This ensures that each entity is uniquely represented within the master data repository.
* Data Model Management:
* Focuses on defining and maintaining data models that describe the structure, relationships, and constraints of the data.
* Data Acquisition:
* Involves gathering and bringing data into the MDM system but does not deal with resolving entities.
* Entity Resolution:
* This process involves matching and linking records from different sources that refer to the same entity. Techniques such as deterministic matching (based on exact matches) and probabilistic matching (based on similarity scores) are used.
* Entity resolution helps in deduplication and ensuring a single, unified view of each entity within the MDM system.
* Data Sharing & Stewardship:
* Focuses on managing data access and ensuring that data is shared responsibly and accurately.
* Data Validation, Standardization, and Enrichment:
* Ensures data quality by validating, standardizing, and enriching data but does not directly address entity resolution.

NEW QUESTION # 92
MDM matching algorithms benefit from all of the following data characteristics except for which of the following?

A. High validity of the data
B. Low number of common data points
C. High level of comparability of the data elements
D. Distinctiveness across the population of data
E. Structural heterogeneity of data elements

Answer: E

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.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide

NEW QUESTION # 93
The easiest MDM style to implement data governance based on controls that can be placed on persistent data is:

A. Centralized style
B. Agile Style
C. Consolidation style
D. Registry style
E. Multi-hub

Answer: A

Explanation:
The centralized style is the easiest MDM style to implement data governance because it consolidates all master data into a single central repository. This centralization simplifies the application of data governance controls, ensuring consistent data quality, standards, and policies are applied across the organization.
References:
* DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.
* Master Data Management and Data Governance by Alex Berson and Larry Dubov.

NEW QUESTION # 94
What is the best way to ensure you have high quality reference data?

A. Only use standard reference data provided by ISO
B. Only use reference data from government sources
C. Implement Data Governance and Stewardship
D. Only use data from external data providers
E. Create drop-down menus for data entry to prevent all invalid data

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.

NEW QUESTION # 95
Which of the following is NOT a metric that c.tn be tied to Reference and Master Data Quality?

A. Operational functions
B. Data sharing usage
C. Data sharing volume
D. The rate of change of data values
E. Service Level Agreements

Answer: A

Explanation:
Metrics tied to Reference and Master Data Quality generally include:
* Data Sharing Usage: Measures how often master data is accessed and used across the organization.
* Rate of Change of Data Values: Tracks how frequently master data values are updated or modified.
* Service Level Agreements (SLAs): Monitors adherence to agreed-upon service levels for data availability, accuracy, and timeliness.
* Data Sharing Volume: Measures the volume of data shared between systems or departments.
* Excluded Metric - Operational Functions: While operational functions are important, they are not typically considered metrics for data quality. Operational functions refer to the various tasks and processes performed by systems and personnel but do not directly measure data quality.
* References:
* Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

NEW QUESTION # 96
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Achieve an Excellent Score in Your DAMA CDMP-RMD Exam with Exam4Labs