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2025 Practice CDMP-RMD Test - Trustable DAMA Instant CDMP-RMD Discount: Reference And Master Data Management Exam

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DAMA CDMP-RMD Exam Syllabus Topics:

Topic	Details
Topic 1	MDM and RDM Tools and Technologies: The technologies and tools available for managing reference and master data are discussed in this section.
Topic 2	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 3	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 4	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 5	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 6	Reference Data Management (RDM) Lifecycle: The lifecycle of reference data management is covered in this topic.

DAMA Reference And Master Data Management Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which of the following Is a characteristic of a probabilistic matching algorithm?

A. A score is assigned based on weight and degree of match
B. Each variable to be matched is assigned a weight based on its discriminating power
C. All answers are correct
D. Individual attribute matching scores arc used to create a match probability percentage.
E. Following the matching process there are typically records requiring manual review and decisioning.

Answer: C

Explanation:
Probabilistic matching algorithms assign a score based on the weight and degree of match, assign weights to variables based on their discriminating power, and use individual attribute matching scores to create a match probability percentage. Additionally, after the matching process, some records typically require manual review and decisioning to ensure accuracy. Therefore, all provided characteristics describe the nature of probabilistic matching algorithms accurately.
References:
* DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.
* "Master Data Management and Data Governance" by Alex Berson and Larry Dubov

NEW QUESTION # 74
Which of the following isNOT part of MDM Lifecycle Management?

A. Identifying multiple instances of the same entity
B. Establishing recovery and backup rules
C. Identifying improperly matched or merged instances of data
D. Maintaining cross-references to enable information integration
E. Reconciling and consolidating data

Answer: B

Explanation:
Master Data Management (MDM) lifecycle management encompasses the processes and practices involved in managing master data throughout its lifecycle, from creation to retirement. It ensures that master data remains accurate, consistent, and usable.
* Reconciling and Consolidating Data:
* This process involves merging data from multiple sources to create a single, unified view of each master data entity.
* It ensures that duplicate records are identified and consolidated, maintaining data consistency.
* Identifying Multiple Instances of the Same Entity:
* This involves detecting and resolving duplicate records to ensure that each master data entity is uniquely represented.
* Tools and algorithms are used to identify potential duplicates based on matching criteria.
* Identifying Improperly Matched or Merged Instances of Data:
* This step involves reviewing and correcting any errors that occurred during the matching or merging process.
* Ensures that data integrity is maintained and that merged records accurately represent the underlying entities.
* Maintaining Cross-References to Enable Information Integration:
* Cross-references link related data entities across different systems, enabling seamless information integration.
* This ensures that data can be consistently accessed and used across the organization.
* Establishing Recovery and Backup Rules (NOT part of MDM Lifecycle Management):
* While important for overall data management, recovery and backup rules pertain more to data protection and disaster recovery rather than the specific processes of MDM lifecycle management.

NEW QUESTION # 75
These are two metrics you must produce totrackthe effectiveness of your Reference and Master Data Program:

A. Data model Validation and Measurement
B. Data Quality and Security Incident Metrics
C. Data Quality and Data Consumption Trends in implementation and Access Control
D. Value and sustainability

Answer: C

Explanation:
Tracking the effectiveness of a Reference and Master Data Management (RMDM) program requires monitoring various metrics that reflect the quality, usage, and governance of the data.The key metrics in this context are Data Quality and Data Consumption Trends, along with Access Control.
* Data Quality:
* Data quality metrics assess the accuracy, completeness, consistency, and reliability of the master and reference data.
* Common data quality metrics include:
* Accuracy:Correctness of data values.
* Completeness:Presence of all required data values.
* Consistency:Uniformity of data across different systems.
* Timeliness:Up-to-date and current data.
* Tracking data quality helps identify issues and areas for improvement, ensuring that the data remains fit for purpose.
* Data Consumption Trends:
* Monitoring data consumption trends involves analyzing how data is used across the organization.
* This includes tracking the frequency and volume of data access, the number of users accessing the data, and the business processes that depend on the data.
* Understanding consumption trends helps in identifying critical data assets, optimizing data delivery, and ensuring that the data meets the needs of its users.
* Access Control:
* Access control metrics track the security and governance of master and reference data.
* This includes monitoring who has access to the data, how the data is accessed, and any unauthorized access attempts.
* Ensuring proper access control is crucial for data security and compliance with regulatory requirements.
* Value and Sustainability:
* While important, these metrics focus more on the overall value and long-term viability of the RMDM program rather than specific operational effectiveness.

NEW QUESTION # 76
Which of the following is NOT a possible outcome of a probabilistic matching algorithm?

A. Match
B. None of answers are correct (
C. Non- match
D. Underminable match
E. Likely Match

Answer: D

Explanation:
* Understanding Probabilistic Matching:Probabilistic matching algorithms are used in data matching processes to compare records and determine if they refer to the same entity. These algorithms use statistical techniques to calculate the likelihood of matches.
* Possible Outcomes of Probabilistic Matching:
* Likely Match:The algorithm determines that the records are probably referring to the same entity based on calculated probabilities.
* Non-match:The algorithm determines that the records do not refer to the same entity.
* Match:The algorithm determines with high confidence that the records refer to the same entity.
* Non-Standard Outcome (D):The term "Underminable match" is not a standard term used in probabilistic matching outcomes. Typically, if the algorithm cannot determine a match or non-match, it might categorize it as "possible match" or leave it undecided but not as "underminable."
* Conclusion:The term "Underminable match" does not fit into the standard categories of probabilistic matching outcomes.
References:
* DMBOK Guide, specifically the sections on Data Quality and Data Matching Techniques.
* Industry standards and documentation on probabilistic data matching algorithms.

NEW QUESTION # 77
The most difficult MDM style to implement data governance is which of following-

A. Consolidation style
B. Linkage style
C. Coexistence style
D. Registry style
E. Centralized style

Answer: D

Explanation:
The registry style is the most difficult MDM style to implement data governance due to its reliance on maintaining a central registry of master data without consolidating data physically. This method makes it challenging to ensure consistent governance across disparate systems since data remains distributed and only loosely connected via the registry.
References:
* DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.
* Master Data Management: Creating a Single Source of Truth by David Loshin.

NEW QUESTION # 78
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