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Free CDMP-RMD Exam Braindumps (page: 12)

Page 11 of 26
PDF with 100 Questions

Location related attributes used exclusively by a group of Financial applications are considered as:

  1. Reference Data
  2. Metadata
  3. Application Suite Master Data
  4. Application Master Data
  5. Enterprise Master Data

Answer(s): D

Explanation:

Understanding the Context: Location-related attributes are specific details that describe the physical or logical location of an entity. These attributes can include information such as geographical coordinates, address details, or logical identifiers used in software applications.
Categories of Data:
Reference Data: This is data that is used to define other data. It often includes code lists, taxonomies, and hierarchies. Examples are country codes or currency codes. Metadata: This is data about data, providing context or additional information about other data. Examples include schema definitions or data dictionaries. Application Suite Master Data: This refers to the master data used across an entire suite of applications but not necessarily enterprise-wide.
Application Master Data: This is master data specific to a single application or a closely related group of applications within a specific function.
Enterprise Master Data: This is master data that is used across the entire enterprise, supporting multiple functions and applications.
Application Master Data Identification: The question specifies that these location-related attributes are used exclusively by a group of financial applications. This exclusivity implies that the data is tailored for specific applications rather than being used across the entire enterprise or just for reference purposes.
Conclusion: Since the data is used specifically within a group of financial applications, it best fits the category of "Application Master Data" rather than enterprise-wide or reference data.


Reference:

DMBOK Guide: Data Management Body of Knowledge, specifically sections on Data Governance and Master Data Management.



Which of the following is NOT a possible outcome of a probabilistic matching algorithm?

  1. Likely Match
  2. Non- match
  3. None of answers are correct (
  4. Underminable match
  5. Match

Answer(s): 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.


Reference:

DMBOK Guide, specifically the sections on Data Quality and Data Matching Techniques. Industry standards and documentation on probabilistic data matching algorithms.



Why is a historical perspective of Master Data important?

  1. Provides an audit trail
  2. May be required in litigation cases
  3. Attributes about Master Data subjects evolve over time
  4. Enables business analytics to determine the root cause of behavioral changes
  5. All of the above

Answer(s): E

Explanation:

Historical Perspective of Master Data: Maintaining historical data about master data objects is crucial for various reasons.
Reasons for Importance:
Provides an audit trail: Keeping historical data allows organizations to track changes and understand the evolution of data over time, which is essential for auditing purposes. May be required in litigation cases: Historical data can serve as evidence in legal disputes, demonstrating the state of data at specific points in time. Attributes about Master Data subjects evolve over time: As entities change, such as customers moving or changing names, maintaining historical data allows for accurate tracking of these changes. Enables business analytics to determine the root cause of behavioral changes: Historical data can help in analyzing trends and identifying reasons for changes in business metrics or customer behavior.
Conclusion: All the provided reasons collectively highlight the importance of maintaining a historical perspective of master data.


Reference:

DMBOK Guide, sections on Master Data Management and Data Governance.
CDMP Examination Study Materials.



A division of power approach to master data governance provides the benefit of:

  1. Better alignment of decisions based on varying levels of organizational data sharing
  2. Spreads the blame for bad decisions
  3. Centralizing responsibility
  4. Lower expense
  5. Facilitating a decision by committee model

Answer(s): A

Explanation:

Division of Power in Data Governance: This approach distributes decision-making authority across different levels or areas within the organization.
Benefits:
Better alignment of decisions: By distributing power, decisions can be made that are better suited to the specific needs and contexts of different parts of the organization. This ensures that decisions about data management are relevant and effective for each particular area. Avoids centralization issues: Centralized decision-making can often be disconnected from the needs of different departments or functions.
Improved responsiveness:
Decentralized governance can enable faster and more contextually appropriate responses to data management issues.
Other Options Analysis:
Spreads the blame for bad decisions: This is not a strategic benefit but rather a negative consequence.
Centralizing responsibility: This contradicts the concept of division of power. Lower expense: While decentralization might lead to better decision-making, it doesn't inherently mean lower costs.
Facilitating a decision by committee model: This can lead to slower decision-making processes and isn't the primary benefit of a division of power.
Conclusion: The key benefit of a division of power approach in master data governance is the better alignment of decisions based on varying levels of organizational data sharing.


Reference:

DMBOK Guide, sections on Data Governance and Organizational Structures.
CDMP Examination Study Materials.






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