Free Practice Questions for DAMA CDMP-RMD Exam

International Classification of Diseases (ICD) codes are a type of industry reference data.

ICD Codes:

Developed by the World Health Organization (WHO), ICD codes are used globally to classify and code all diagnoses, symptoms, and procedures recorded in conjunction with hospital care.

They are essential for health care management, epidemiology, and clinical purposes.

Industry Reference Data:

Industry reference data pertains to standardized data used within a particular industry to ensure consistency, accuracy, and interoperability.

ICD codes fall into this category as they are standardized across the healthcare industry, facilitating uniformity in data reporting and analysis.

Other Options:

Geographic Reference Data:Includes data like country codes, region codes, and GPS coordinates.

Computational Reference Data:Used in computational processes and algorithms.

Internal Reference Data:Data used internally within an organization that is not standardized across industries.

Entity resolution is a critical step in the MDM process that identifies whether different data records refer to the same real-world entity. This ensures that each entity is uniquely represented within the master data repository.

Data Model Management:

Focuses on defining and maintaining data models that describe the structure, relationships, and constraints of the data.

Data Acquisition:

Involves gathering and bringing data into the MDM system but does not deal with resolving entities.

Entity Resolution:

This process involves matching and linking records from different sources that refer to the same entity. Techniques such as deterministic matching (based on exact matches) and probabilistic matching (based on similarity scores) are used.

Entity resolution helps in deduplication and ensuring a single, unified view of each entity within the MDM system.

Data Sharing & Stewardship:

Focuses on managing data access and ensuring that data is shared responsibly and accurately.

Data Validation, Standardization, and Enrichment:

Ensures data quality by validating, standardizing, and enriching data but does not directly address entity resolution.

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.

MDM (Master Data Management) is a lifecycle management process that includes various activities to ensure the quality, consistency, and accessibility of master data across an organization. These activities include:

Provisioning of Access: Ensuring that trusted master data is accessible across applications through various methods such as direct reads, data services, or replication feeds.

Identifying Multiple Instances: Detecting and managing multiple representations of the same entity within and across data sources. This involves creating and maintaining identifiers and cross-references for integration.

Enforcing Use of Master Data: Ensuring that the organization consistently uses master data values in processes and applications.

Resolving Improper Matches: Identifying and resolving improperly matched or merged data instances to maintain data integrity.

The activity of "Ensuring effective and efficient retrieval and use of data and information by ETL logic" (C) is not specific to MDM. While ETL (Extract, Transform, Load) processes are crucial for data integration and warehousing, they are not a core activity unique to the MDM lifecycle.

References:

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

"Master Data Management and Data Governance" by Alex Berson and Larry Dubov.

The three primary categories of components in a Master Data Management (MDM) framework are people, process, and technology. Here’s a detailed breakdown:

People:

Roles and Responsibilities: Involves defining roles such as data stewards, data owners, and data governance committees who are responsible for managing and overseeing master data.

Skills and Training: Ensuring that the individuals involved have the necessary skills and training to manage master data effectively.

Process:

Data Governance: Establishing policies, procedures, and standards for managing master data to ensure its accuracy, consistency, and reliability.

Data Lifecycle Management: Processes for creating, maintaining, and retiring master data.

Technology:

MDM Tools and Platforms: Utilizing technology solutions to support the management of master data, including data integration, data quality, and data management platforms.

Infrastructure: Ensuring the necessary technical infrastructure is in place to support MDM activities.

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)"

Master data metadata provides crucial information about the master data, offering context and supporting its management and use within the organization.

Who, What, and Where Context:

Metadata provides descriptive information about the master data, including details about who created or modified the data, what the data represents, and where it is used.

This contextual information is essential for understanding the origins, purpose, and usage of the master data.

Includes a Sample of Content:

While metadata might include examples or samples of the data, this is not its primary purpose.

Improving Fit-for-Purpose Choices:

Metadata helps improve the application and governance of master data by providing context and supporting data management decisions.

Securing the Content:

Metadata itself is not primarily focused on security, though it can support data governance and access control processes.

Technical or Business Perspectives:

Metadata can encompass both technical and business perspectives, providing a holistic view of the data's context and usage.

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.

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)"

Historical Perspective of Master Data:Maintaining historical data about master data objects is crucial for various reasons.

Reasons for Importance:

Provides an audit trail:Keeping historical data allows organizations to track changes and understand the evolution of data over time, which is essential for auditing purposes.

May be required in litigation cases:Historical data can serve as evidence in legal disputes, demonstrating the state of data at specific points in time.

Attributes about Master Data subjects evolve over time:As entities change, such as customers moving or changing names, maintaining historical data allows for accurate tracking of these changes.

Enables business analytics to determine the root cause of behavioral changes:Historical data can help in analyzing trends and identifying reasons for changes in business metrics or customer behavior.

Conclusion:All the provided reasons collectively highlight the importance of maintaining a historical perspective of master data.

References:

DMBOK Guide, sections on Master Data Management and Data Governance.

CDMP Examination Study Materials.

Master Data Management (MDM) lifecycle management encompasses the processes and practices involved in managing master data throughout its lifecycle, from creation to retirement. It ensures that master data remains accurate, consistent, and usable.

Reconciling and Consolidating Data:

This process involves merging data from multiple sources to create a single, unified view of each master data entity.

It ensures that duplicate records are identified and consolidated, maintaining data consistency.

Identifying Multiple Instances of the Same Entity:

This involves detecting and resolving duplicate records to ensure that each master data entity is uniquely represented.

Tools and algorithms are used to identify potential duplicates based on matching criteria.

Identifying Improperly Matched or Merged Instances of Data:

This step involves reviewing and correcting any errors that occurred during the matching or merging process.

Ensures that data integrity is maintained and that merged records accurately represent the underlying entities.

Maintaining Cross-References to Enable Information Integration:

Cross-references link related data entities across different systems, enabling seamless information integration.

This ensures that data can be consistently accessed and used across the organization.

Establishing Recovery and Backup Rules (NOT part of MDM Lifecycle Management):

While important for overall data management, recovery and backup rules pertain more to data protection and disaster recovery rather than the specific processes of MDM lifecycle management.