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DAMA Reference And Master Data Management Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
The Master Data hub environment that serves as the system of record tor Master Data is:

A. Source Hub
B. SOA
C. Registry
D. Two-Speed Hub
E. Consolidated Hub

Answer: E

Explanation:
The Master Data hub environment that serves as the system of record for Master Data is:
* Consolidated Hub:
* Central Repository: Acts as a central repository where master data is stored and managed.
* Data Quality and Integration: Ensures data quality by integrating data from various source systems and providing a single source of truth.
* System of Record: Maintains the most accurate and up-to-date information about master data entities.
* Other Hub Types:
* SOA (Service-Oriented Architecture): Focuses on providing a flexible architecture for integrating services but not specifically a master data hub.
* Two-Speed Hub: A hybrid approach, but not solely a system of record.
* Source Hub: May refer to original source systems, not a consolidated system of record.
* Registry: Primarily maintains references to data stored in other systems but not a comprehensive system of record.
* 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 # 73
What role would you expect Data Governance to play in the development of an enterprise wide MDM strategy?

A. Developing xml for data messaging.
B. Helping the DBAs design efficient database tables
C. Identify different approaches to data processing.
D. Producing and managing an enterprise conceptual data model to focus and support the MDM strategy
E. Identify data sources to be integrated

Answer: D

Explanation:
Data Governance plays a pivotal role in the development of an enterprise-wide Master Data Management (MDM) strategy. Here's how:
* Role of Data Governance:
* Policy Development: Data Governance establishes policies and standards for data management to ensure data quality, security, and compliance.
* Data Stewardship: Assigns roles and responsibilities to manage and oversee data assets across the organization.
* MDM Strategy Support:
* Conceptual Data Model:
* Producing and managing an enterprise conceptual data model helps align the organization's data architecture with its business processes.
* It provides a unified view of data entities, their relationships, and how data flows through various systems, ensuring consistency and accuracy.
* Alignment with Business Goals: Ensures that MDM efforts support business objectives by providing a clear framework for data usage and governance.
* References:
* Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

NEW QUESTION # 74
Should both in-house and commercial tools meet ISO standards for metadata?

A. Yes. at the very least they should provide guidance
B. No. each organization needs to develop their own standards based on needs

Answer: A

Explanation:
Adhering to ISO standards for metadata is important for both in-house and commercial tools for the following reasons:
* Standardization:
* Uniformity: ISO standards ensure that metadata is uniformly described and managed across different tools and systems.
* Interoperability: Facilitates interoperability between different tools and systems, enabling seamless data exchange and integration.
* Guidance and Best Practices:
* Structured Approach: Provides a structured approach for defining and managing metadata, ensuring consistency and reliability.
* Compliance and Quality: Ensures compliance with internationally recognized best practices, enhancing data quality and governance.
* References:
* ISO/IEC 11179: Information technology - Metadata registries (MDR)
* 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 # 75
Which of the following is NOT a possible outcome of a probabilistic matching algorithm?

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

Answer: E

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 # 76
The format and allowable ranges of Master Data values are dictated by:

A. Business rules
B. Semantic rules
C. Engagement rules
D. Database limitations
E. Processing rules

Answer: A

Explanation:
The format and allowable ranges of Master Data values are primarily dictated by business rules.
* Business Rules:
* Business rules define the constraints, formats, and permissible values for master data based on the organization's operational and regulatory requirements.
* These rules ensure that data conforms to the standards and requirements necessary for effective business operations.
* Semantic Rules:
* These rules pertain to the meaning and context of the data but do not directly dictate formats and ranges.
* Processing Rules:
* These rules focus on how data is processed but not on the allowable values or formats.
* Engagement Rules:
* These rules govern interactions and workflows rather than data formats and ranges.
* Database Limitations:
* While database limitations can impose constraints, they are typically secondary to the business rules that drive data requirements.

NEW QUESTION # 77
......

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