CDMP-RMD Exam Dumps Pass with Updated 2024 Certified Exam Questions
CDMP-RMD Exam Questions - Real & Updated Questions PDF
NEW QUESTION # 14
Which of the following is NOT a metric that c.tn be tied to Reference and Master Data Quality?
- A. Data sharing volume
- B. Operational functions
- C. Service Level Agreements
- D. The rate of change of data values
- E. Data sharing usage
Answer: B
Explanation:
Metrics tied to Reference and Master Data Quality generally include:
* Data Sharing Usage: Measures how often master data is accessed and used across the organization.
* Rate of Change of Data Values: Tracks how frequently master data values are updated or modified.
* Service Level Agreements (SLAs): Monitors adherence to agreed-upon service levels for data availability, accuracy, and timeliness.
* Data Sharing Volume: Measures the volume of data shared between systems or departments.
* Excluded Metric - Operational Functions: While operational functions are important, they are not typically considered metrics for data quality. Operational functions refer to the various tasks and processes performed by systems and personnel but do not directly measure data quality.
* 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)"
NEW QUESTION # 15
Does anorganizationhave to agree to a single definition for Master Data?
- A. No. master data can have many definitions
- B. No. financial data is master data but the definition is always changing
- C. Yes. the key thing is to agree on a standard definition
- D. No. each department can have their own definitions for master data
- E. No. technical data may have many definitions depending on the vendor
Answer: C
Explanation:
For effective Master Data Management, an organization must agree on a single, standard definition of master data. Here's why:
* Consistency:
* Single Definition: A standardized definition ensures consistency across different departments and systems.
* Avoids Confusion: Prevents discrepancies and misunderstandings regarding what constitutes master data.
* Data Quality and Governance:
* Unified Approach: A single definition supports unified data governance policies and data quality standards.
* Data Integration: Facilitates easier data integration and interoperability across various systems and processes.
* Business Efficiency:
* Aligned Objectives: Ensures all parts of the organization are aligned in their understanding and use of master data, leading to more efficient operations and decision-making.
* 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)"
NEW QUESTION # 16
Is there a standard tor defining and exchanging Master Data?
- A. Yes, ISO 22745
- B. Yes. it is called ETL
- C. No. there are no standards because not everyone uses Master Data
- D. No. every corporation uses their own method
Answer: A
Explanation:
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.
NEW QUESTION # 17
The MDM process step responsible for determining whether two references to real world objects refer to the same object or different objects is known as:
- A. Data Sharing & Stewardship
- B. Entity Resolution
- C. Data Model Management
- D. Data Validation. Standardization, and Enrichment
- E. Data Acquisition
Answer: B
Explanation:
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.
NEW QUESTION # 18
Every process within a MDM framework includes:
- A. Automation of all process tasks
- B. Data enrichment
- C. Reference data
- D. A separate data steward
- E. A degree of governance
Answer: E
Explanation:
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.
* 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)"
NEW QUESTION # 19
The 3 primary categories of components in a MDM framework are:
- A. Program, project, task
- B. Structure, ETL, & storage
- C. People, process, & technology
- D. Matching, linking. & verification
- E. Integration, quality, & governance
Answer: C
Explanation:
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)"
NEW QUESTION # 20
Matching or candidate identification is the process called similarity analysis. One approach is called deterministic which relies on:
- A. Algorithms for parsing and standardization and on defined patterns and rules for determining similarity
- B. Finding two references that are linked with a single entity
- C. Being able to determine the similarity between two data models
- D. Taking data samples and looking at results for a subset of the records
- E. Statistical techniques for assessing the probability that any pair of records represents the same entity
Answer: A
Explanation:
Deterministic matching, also known as exact matching, relies on predefined rules and algorithms to parse and standardize data, ensuring that records are compared based on exact or standardized values. This approach uses defined patterns and rules to determine whether two records represent the same entity by matching key attributes exactly. Deterministic matching is precise and unambiguous, making it a common approach for high-certainty matching tasks, although it can be less flexible than probabilistic methods that allow for variations in data.
References:
* DAMA-DMBOK2 Guide: Chapter 10 - Master and Reference Data Management
* "Entity Resolution and Information Quality" by John R. Talburt
NEW QUESTION # 21
The Reference Data Change Request Process does NOT include which of the following subprocesses:
- A. Receive Change Request
- B. Monitor Database Change
- C. Decide and Communicate
- D. Identify Impact
- E. Identify Stakeholder
Answer: B
Explanation:
The Reference Data Change Request Process typically involves the following sub-processes:
* Receive Change Request:
* Initiation: The process begins with the receipt of a change request, formally logged and acknowledged.
* Identify Stakeholder:
* Stakeholder Identification: Identifying all relevant stakeholders who need to be involved or informed about the change.
* Identify Impact:
* Impact Analysis: Assessing the potential impact of the requested change on existing systems, processes, and data.
* Decide and Communicate:
* Decision Making: Reviewing the change request, making a decision, and communicating the outcome to stakeholders.
* Excluded Step - Monitor Database Change: While monitoring database changes is important for overall data management, it is not typically part of the specific change request process for reference data. This step pertains more to ongoing operational monitoring rather than the change request workflow.
* References:
* Data Management Body of Knowledge (DMBOK), Chapter 6: Data Development & Maintenance
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
NEW QUESTION # 22
What role would you expect Data Governance to play in the development of an enterprise wide MDM strategy?
- A. Producing and managing an enterprise conceptual data model to focus and support the MDM strategy
- B. Identify different approaches to data processing.
- C. Helping the DBAs design efficient database tables
- D. Developing xml for data messaging.
- E. Identify data sources to be integrated
Answer: A
Explanation:
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.
* References:
* Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
NEW QUESTION # 23
International Classification of Diseases (ICD) codes are an example of:
- A. Computational Reference Data
- B. Industry Reference Data
- C. None of these
- D. Internal Reference Data
- E. Geographic Reference Data
Answer: B
Explanation:
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.
NEW QUESTION # 24
Can the kinds of information treated as master data vary from one industry to another and even from one company to another within the same industry?
- A. No. master data for an industry is always standardized
- B. No. master data is always the same kind of information
- C. Yes. each industry and/or company has their own core master data
Answer: C
Explanation:
Master data refers to the critical data that is essential to the operations of a business. It typically includes entities such as customers, products, employees, suppliers, and other key business entities. The kinds of information treated as master data can vary widely between industries and even between companies within the same industry.
* Industry-Specific Master Data:
* Different industries have distinct core data entities critical to their operations. For example, in the healthcare industry, patient and provider data are crucial, whereas, in the retail industry, product and customer data are paramount.
* Companies in regulated industries may have specific master data requirements mandated by regulatory bodies.
* Company-Specific Master Data:
* Within the same industry, different companies may prioritize different sets of master data based on their unique business processes, strategies, and operational needs.
* Organizational size, structure, and business model can influence what is considered master data.
* Customization and Flexibility:
* Master data management (MDM) systems and practices are designed to be flexible to accommodate the unique needs of different organizations.
* Customizing MDM allows companies to manage and maintain the integrity of the specific data entities that are critical to their success.
NEW QUESTION # 25
MOM Harmonization ensures that the data changes of one application:
- A. Are recorded in the repository or data dictionary
- B. Has a data steward to preview the data for quality
- C. include changes to the configuration of the database as well as the data
- D. Agree with the overall MDM architecture
- E. Are synchronized with all other applications who depend on that data
Answer: E
Explanation:
Master Data Management (MDM) Harmonization ensures that the data changes of one application are synchronized with all other applications that depend on that data.
* MDM Harmonization Definition:This process involves aligning and reconciling data from different sources to ensure consistency and accuracy across the enterprise.
* Synchronization:Ensuring that changes in one application are reflected across all dependent applications prevents data inconsistencies and maintains data integrity.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide
NEW QUESTION # 26
A key capability to quickly onboard new data suppliers and subscribers to a MDM solution is which of the following?
- A. Encrypting all personal information
- B. Data format and transfer flexibility
- C. Source system conformance to a single standard data input format
- D. Subscriber conformance to a single standard data output format
- E. Requiring only delta loads of changed data attributes
Answer: B
Explanation:
* Definitions and Context:
* MDM Solution: This involves tools and processes to manage master data within an organization to ensure a single source of truth.
* Onboarding Data Suppliers and Subscribers: This process involves integrating new data sources (suppliers) and distributing data to various applications or users (subscribers).
* Explanation:
* A key capability for onboarding is the flexibility in data format and transfer methods because different data suppliers may use various formats and protocols.
* Ensuring flexibility allows the MDM system to easily adapt to different data sources and meet the needs of diverse data consumers, thereby facilitating quick and efficient onboarding.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
* The Open Group, "TOGAF Series Guide: The Data Management Capability Assessment Model (DCAM)".
NEW QUESTION # 27
_____ are a primary supplier of master data content to a MDM program.
- A. Point of sale systems
- B. Configuration management database (CMDB)
- C. Systems of record
- D. Data catalog
- E. Business intelligence applications
Answer: C
Explanation:
Systems of record are primary suppliers of master data content to an MDM program.
* Systems of Record:These are authoritative data sources that provide consistent and reliable master data.
* Role in MDM:They supply accurate and up-to-date master data, ensuring that the MDM system has a solid foundation of information.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide
NEW QUESTION # 28
Should both in-house and commercial tools meet ISO standards for metadata?
- A. No. each organization needs to develop their own standards based on needs
- B. Yes. at the very least they should provide guidance
Answer: B
Explanation:
Adhering to ISO standards for metadata is important for both in-house and commercial tools for the following reasons:
* Standardization:
* Uniformity: ISO standards ensure that metadata is uniformly described and managed across different tools and systems.
* Interoperability: Facilitates interoperability between different tools and systems, enabling seamless data exchange and integration.
* Guidance and Best Practices:
* Structured Approach: Provides a structured approach for defining and managing metadata, ensuring consistency and reliability.
* Compliance and Quality: Ensures compliance with internationally recognized best practices, enhancing data quality and governance.
* References:
* ISO/IEC 11179: Information technology - Metadata registries (MDR)
* Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
* DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
NEW QUESTION # 29
An organization chart where a high level manager has department managers with staff and non-managers without staff as direct reports would best be maintained in which of the following?
- A. A data dictionary
- B. A ragged hierarchy
- C. A taxonomy
- D. A reference file
- E. A fixed level hierarchy
Answer: B
Explanation:
A ragged hierarchy is an organizational structure where different branches of the hierarchy can have varying levels of depth. This means that not all branches have the same number of levels. In the given scenario, where a high-level manager has department managers with staff and non-managers without staff as direct reports, the hierarchy does not have a uniform depth across all branches. This kind of structure is best represented and maintained as a ragged hierarchy, which allows for flexibility in representing varying levels of managerial relationships and reporting structures.
References:
* DAMA-DMBOK2 Guide: Chapter 7 - Data Architecture Management
* "Master Data Management and Data Governance" by Alex Berson, Larry Dubov
NEW QUESTION # 30
For MDMs. what is meant by a classification scheme?
- A. Codes that represent a controlled set of values
- B. Descriptive language used to control objects
- C. A way of classifying unstructured data
- D. A vocabulary view covering a limited range of topics
Answer: A
Explanation:
In Master Data Management (MDM), a classification scheme refers to a structured way of organizing data by using codes that represent a controlled set of values. These codes help in categorizing and standardizing data, making it easier to manage, search, and analyze.
References:
* 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.
NEW QUESTION # 31
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