Dama Reference And Master Data Management CDMP-RMD Exam Questions

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 : D

Probabilistic matching algorithms assign a score based on the weight and degree of match, assign weights to variables based on their discriminating power, and use individual attribute matching scores to create a match probability percentage. Additionally, after the matching process, some records typically require manual review and decisioning to ensure accuracy. Therefore, all provided characteristics describe the nature of probabilistic matching algorithms accurately.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.

'Master Data Management and Data Governance' by Alex Berson and Larry Dubov

Answer : C

Reference data and master data are distinct in several key characteristics. Here's a detailed explanation:

Reference Data Characteristics:

Stability: Reference data is generally less volatile and changes less frequently compared to master data.

Complexity: It is less complex, often consisting of simple lists or codes (e.g., country codes, currency codes).

Size: Reference data sets are typically smaller in size than master data sets.

Master Data Characteristics:

Volatility: Master data can be more volatile, with frequent updates (e.g., customer addresses, product details).

Complexity: More complex structures and relationships, involving multiple attributes and entities.

Size: Larger in size due to the detailed information and numerous entities it encompasses.

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

In the context of the Corporate Information Factory, understanding transactions involves integrating various types of data to get a comprehensive view. Master Data (core business entities), Reference Data (standardized information), and External Data (information sourced from outside the organization) are essential for providing context and enriching transactional data.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 3: Data Architecture and Chapter 11: Reference and Master Data Management.

'Building the Data Warehouse' by W.H. Inmon, which introduces the Corporate Information Factory concept.

Answer : C

Planned control activities are not considered master data. Here's why:

Master Data Examples:

Categories and Lists: Master data typically includes lists and categorizations that are used repeatedly across multiple business processes and systems.

Examples: Product categories, account codes, country codes, and currency codes, which are relatively stable and broadly used.

Planned Control Activities:

Process-Specific: Planned control activities pertain to specific actions and checks within business processes, often linked to operational or transactional data.

Not Repeated Data: They are not reused or referenced as a stable entity across different systems.

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 : A

Key processing steps for successful MDM typically include:

Data Acquisition: The process of gathering and importing data from various sources.

Data Sharing & Stewardship: Involves ensuring data is shared appropriately across the organization and that data stewards manage data quality and integrity.

Entity Resolution: Identifying and linking data records that refer to the same entity across different data sources.

Data Model Management: Creating and maintaining data models that define how data is structured and related within the MDM system.

Excluded Step - Data Indexing: While indexing is a critical database performance optimization technique, it is not a primary processing step specific to MDM. MDM focuses on consolidating, managing, and ensuring the quality of master data rather than indexing, which is more about search optimization within databases.

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 : B

Definitions and Context:

Reference Data Dictionaries: These are authoritative resources that provide standardized definitions and classifications for data elements.

External Sources of Data: These are data sources that come from outside the organization and are used for various analytical and operational purposes.

Reference Data Dictionaries often contain listings and definitions for data that are used across different organizations and systems, ensuring consistency and interoperability.

They typically include external data sources, which need to be standardized and understood in the context of the organization's own data environment.

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.

ISO/IEC 11179-3:2013, Information technology - Metadata registries (MDR) - Part 3: Registry metamodel and basic attributes.