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DAMA Reference And Master Data Management Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
Choosing unreliable sources for data, which can cause data quality issues, is a result of:

A. Too much data
B. Immature data architecture
C. No chance controls
D. Too little data
E. Weak Master Data Management

Answer: E

Explanation:
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.

NEW QUESTION # 51
Can the kinds of information treated as master data vary from one industry to another and even from one company to another within the same industry?

A. No. master data for an industry is always standardized
B. Yes. each industry and/or company has their own core master data
C. No. master data is always the same kind of information

Answer: B

Explanation:
Master data refers to the critical data that is essential to the operations of a business. It typically includes entities such as customers, products, employees, suppliers, and other key business entities. The kinds of information treated as master data can vary widely between industries and even between companies within the same industry.
* Industry-Specific Master Data:
* Different industries have distinct core data entities critical to their operations. For example, in the healthcare industry, patient and provider data are crucial, whereas, in the retail industry, product and customer data are paramount.
* Companies in regulated industries may have specific master data requirements mandated by regulatory bodies.
* Company-Specific Master Data:
* Within the same industry, different companies may prioritize different sets of master data based on their unique business processes, strategies, and operational needs.
* Organizational size, structure, and business model can influence what is considered master data.
* Customization and Flexibility:
* Master data management (MDM) systems and practices are designed to be flexible to accommodate the unique needs of different organizations.
* Customizing MDM allows companies to manage and maintain the integrity of the specific data entities that are critical to their success.

NEW QUESTION # 52
Master and Reference Data are forms of:

A. Data Mapping
B. Data Integration
C. Data Quality
D. Data Architecture
E. Data Security

Answer: D

Explanation:
Master and Reference Data are forms of Data Architecture. Here's why:
* Data Architecture Definition:
* Structure and Design: Data architecture involves the structure and design of data systems, including how data is organized, stored, and accessed.
* Components: Encompasses various components, including data models, data management processes, and data governance frameworks.
* Role of Master and Reference Data:
* Core Components: Master and Reference Data are integral components of an organization's data architecture, providing foundational data elements used across multiple systems and processes.
* Organization and Integration: They play a critical role in organizing and integrating data, ensuring consistency and accuracy.
* 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 # 53
Which of these metrics can be used to measure metadata documentation quality?

A. Currency of metadata in the repository
B. All of these
C. Random survey based on Enterprise definition of quality
D. Percentage of attributes that have definitions
E. Collision Logic on two sources measuring how much they match

Answer: B

Explanation:
Measuring metadata documentation quality involves several metrics that collectively provide a comprehensive view of the quality and effectiveness of metadata management practices.
* Random Survey based on Enterprise Definition of Quality:
* Conducting surveys among data users to gather feedback on the perceived quality of metadata documentation. This helps in understanding user satisfaction and identifying areas for improvement.
* Currency of Metadata in the Repository:
* Ensuring that metadata is up-to-date and accurately reflects the current state of the data. This is crucial for maintaining the relevance and usefulness of metadata.
* Collision Logic on Two Sources Measuring How Much They Match:
* Comparing metadata from different sources to identify discrepancies and ensure consistency. This metric helps in assessing the alignment and accuracy of metadata across systems.
* Percentage of Attributes that have Definitions:
* Measuring the completeness of metadata by checking the percentage of attributes that have well-defined descriptions. This ensures that all data elements are clearly documented and understood.

NEW QUESTION # 54
These are two metrics you must produce totrackthe effectiveness of your Reference and Master Data Program:

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

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 # 55
......

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CDMP-RMD Certified & New CDMP-RMD Test Sims

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DAMA Reference And Master Data Management Exam Sample Questions (Q50-Q55):

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