Dama CDMP-RMD Reference And Master Data Management Exam Practice Test

Answer : D

Every process within an MDM framework includes a degree of governance. Here's why:

Governance Definition:

Policies and Standards: Governance involves the establishment of policies, standards, and procedures to ensure data quality, consistency, and compliance.

Oversight: Provides oversight and accountability for data management practices.

MDM Processes:

Inherent Governance: All MDM processes, from data integration to data quality management, incorporate governance to ensure the integrity and reliability of master data.

Data Stewardship: Involves data stewards who oversee data governance activities, ensuring adherence to established standards and policies.

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

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

All organizations possess master data, even if it is not explicitly labeled as such. Here's why:

Definition of Master Data:

Core Business Entities: Master data refers to the critical entities around which business transactions are conducted, such as customers, products, suppliers, and accounts.

Business Operations: Every organization maintains records of these entities to support business operations, decision-making, and reporting.

Implicit Existence:

Unlabeled Data: Organizations may not explicitly label this data as ''Master Data,'' but it exists within various systems, databases, and spreadsheets.

Examples: Customer lists, product catalogs, employee records, and financial accounts.

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

ISO 22745 is an international standard for defining and exchanging master data.

ISO 22745:

This standard specifies the requirements for the exchange of master data, particularly in industrial and manufacturing contexts.

It includes guidelines for the structured exchange of information, ensuring that data can be shared and understood across different systems and organizations.

Standards for Master Data:

Standards like ISO 22745 help ensure consistency, interoperability, and data quality across different platforms and entities.

They provide a common framework for defining and exchanging master data, facilitating smoother data integration and management processes.

Other Options:

ETL: Refers to the process of Extract, Transform, Load, used in data integration but not a standard for defining master data.

Corporation-specific Methods: Many organizations may have their own methods, but standardized frameworks like ISO 22745 provide a common foundation.

No Standards: While not all organizations use master data, standards do exist for those that do.

ISO 22745 Documentation

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : A

An organization's master data can indeed be acquired from external third parties. Here's how and why:

Third-Party Data Acquisition:

Enrichment: External data sources can be used to enrich an organization's master data, providing additional details and context.

Accuracy and Completeness: Acquiring data from reputable third-party sources can enhance the accuracy and completeness of master data.

Use Cases:

Market Data: Organizations may purchase market data to complement their internal customer or product data.

Reference Data: Common reference data, such as postal codes or industry classifications, are often obtained from external providers.

Integration:

Data Integration: Master data acquired from third parties needs to be integrated into the organization's MDM system, ensuring it aligns with existing data standards and governance policies.

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 : C

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.

Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance

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