Dama Reference And Master Data Management Exam Practice Test

Answer : A

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.

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Answer : D

Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.

Data about Business Entities:

Master data typically includes key entities such as customers, products, suppliers, employees, and locations.

These entities are fundamental to business operations and provide the necessary context for transactions and analysis.

Providing Context for Business Transactions:

Master data provides the foundational information required to conduct business transactions.

For example, customer master data is used in sales transactions, while product master data is used in inventory management.

Supporting Business Analysis:

Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.

It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.

Other Options:

A: Master data and reference data are distinct; reference data is used to categorize master data.

B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.

C: Provides visibility but also context for transactions and analysis.

E: Master data is about business entities, not technical entities.

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Answer : C

Ownership is a crucial principle in managing Reference and Master Data. Here's an in-depth look at why:

Organizational Ownership:

Unified Responsibility: Reference and Master Data are assets that span across various functions and departments within an organization.

Consistency and Accuracy: Ensuring that data ownership is attributed to the organization prevents silos and ensures data is consistently accurate and available across all departments.

Data Governance: Proper governance frameworks ensure that data is managed in a way that meets the organization's needs and complies with relevant regulations and standards.

Avoiding Departmental Silos:

Cross-functional Use: Different departments use and rely on Reference and Master Data, so ownership by a single department can lead to conflicts and inconsistencies.

Holistic Management: Centralized ownership enables holistic data management practices, enhancing data quality and usability across the organization.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Answer : C

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.

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Answer : E

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.

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Answer : D

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.

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Answer : C

Authoritative listings of master data entities are essential for ensuring data accuracy and consistency across an organization.

Canonical Directories:

This term refers to standardized data models but is not typically used to describe authoritative listings of master data entities.

Entity Directories:

While this term could be used, it is not the most accurate or commonly used term for authoritative listings.

Reference Directories:

Reference directories are authoritative lists of master data entities such as companies, people, and products. They provide standardized, trusted, and verified data that organizations can rely on.

These directories ensure that everyone in the organization uses consistent and accurate data, supporting data quality and governance efforts.

Industry Directories:

These directories provide information specific to an industry but are not necessarily authoritative lists of master data entities.

Source Directories:

This term does not specifically refer to authoritative master data listings.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials