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DAMA Reference And Master Data Management Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
Can Reference data be used for financial trading?

A. No. reference data is static, financial data trading is dynamic
B. Yes. an estimated 70% of data being used in financial transactions is reference data
C. Yes. but only less than 1096 can be used
D. No. since financial trades change every second they cannot use reference data
E. No because customer data is not considered reference data

Answer: B

Explanation:
Reference data plays a crucial role in financial trading. It includes data such as financial instrument identifiers, market data, currency codes, and regulatory classifications. Despite the dynamic nature of financial trades, reference data provides the necessary static information to execute and settle transactions. Industry estimates suggest that approximately 70% of the data used in financial transactions is reference data, underscoring its importance in the financial sector.
References:
* DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.
* "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" by Ralph Kimball and Margy Ross.
* Industry publications and whitepapers on reference data management in financial services.

NEW QUESTION # 21
What is the best way to ensure you have high quality reference data?

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

Answer: E

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 # 22
What data architecture component is typically used for ingesting raw source system data?

A. Analytics Zone
B. Curation Zone
C. Landing Zone
D. Presentation Zone
E. Integration Zone

Answer: C

Explanation:
The Landing Zone is typically used for ingesting raw source system data.
* Data Architecture Component:The landing zone is where raw data from various source systems is initially collected and stored.
* Function:It acts as a staging area before the data undergoes further processing, transformation, or analysis.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide

NEW QUESTION # 23
A division of power approach to master data governance provides the benefit of:

A. Centralizing responsibility
B. Facilitating a decision by committee model
C. Lower expense
D. Spreads the blame for bad decisions
E. Better alignment of decisions based on varying levels of organizational data sharing

Answer: E

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

NEW QUESTION # 24
Bringing order to your Master Data would solve what?

A. 60-80% of the most critical data quality problems
B. Distributing data across the enterprise
C. Provide a place to store technical data elements
D. The need for a metadata repository
E. 20 40% of the need to buy new servers

Answer: A

Explanation:
* Definitions and Context:
* Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization's official shared master data assets.
* Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.
* Explanation:
* Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization.
* Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
* Gartner Research, "The Impact of Master Data Management on Data Quality."

NEW QUESTION # 25
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