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

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)'

Answer : E

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.

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.

Answer : A

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:

Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.

Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships.

Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.

By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

'Data Quality: The Accuracy Dimension' by Jack E. Olson.

Answer : D

Understanding the Context: Location-related attributes are specific details that describe the physical or logical location of an entity. These attributes can include information such as geographical coordinates, address details, or logical identifiers used in software applications.

Categories of Data:

Reference Data: This is data that is used to define other data. It often includes code lists, taxonomies, and hierarchies. Examples are country codes or currency codes.

Metadata: This is data about data, providing context or additional information about other data. Examples include schema definitions or data dictionaries.

Application Suite Master Data: This refers to the master data used across an entire suite of applications but not necessarily enterprise-wide.

Application Master Data: This is master data specific to a single application or a closely related group of applications within a specific function.

Enterprise Master Data: This is master data that is used across the entire enterprise, supporting multiple functions and applications.

Application Master Data Identification: The question specifies that these location-related attributes are used exclusively by a group of financial applications. This exclusivity implies that the data is tailored for specific applications rather than being used across the entire enterprise or just for reference purposes.

Conclusion: Since the data is used specifically within a group of financial applications, it best fits the category of 'Application Master Data' rather than enterprise-wide or reference data.

DMBOK Guide: Data Management Body of Knowledge, specifically sections on Data Governance and Master Data Management.

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 : E

Master Data Management (MDM) styles define how and where master data is managed within an organization. One of these styles is the 'Coexistence' style, which allows data to be authored and maintained across different systems while ensuring consistency and synchronization.

Coexistence Style:

The coexistence style of MDM allows master data to be created and updated in multiple locations or systems within an organization.

It supports the integration and synchronization of data across these systems to maintain a single, consistent view of the data.

Key Features:

Data Authoring: Data can be authored and updated in various operational systems rather than being confined to a central hub.

Synchronization: Changes made in one system are synchronized across other systems to ensure data consistency and accuracy.

Flexibility: This style provides flexibility to organizations with complex and distributed IT environments, where different departments or units may use different systems.

Benefits:

Enhances data availability and accessibility across the organization.

Supports operational efficiency by allowing data updates to occur where the data is used.

Reduces the risk of data silos and inconsistencies by ensuring data synchronization.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

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)'