DAMA CDMP-RMD Dumps Questions Answers

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. Thisexclusivity 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.

References:

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

Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.

Too Much Data:

While having excessive data can create challenges, it is not directly related to the reliability of data sources.

Immature Data Architecture:

An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.

Weak Master Data Management (MDM):

MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.

Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.

Too Little Data:

Insufficient data can be problematic but is not directly related to choosing unreliable data sources.

No Chance Controls:

This option is not a standard term in data management and does not directly address the issue of data source reliability.

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