DAMA CDMP-RMD Dumps - 100% Cover Real Exam Questions (Updated 100 Questions)
Real CDMP-RMD dumps - Real DAMA dumps PDF
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NEW QUESTION # 21
What type of interactive system model is most often used for Master Data Management?
- A. Application-coupling
- B. Synchronized interface
- C. Publish-Subscribe
- D. Point-to-point
- E. Hub-and-Spoke
Answer: E
Explanation:
The hub-and-spoke model is most often used for Master Data Management because it provides a central hub where master data is maintained, while the spokes represent different systems or applications that interact with the hub. This model allows for efficient management, synchronization, and distribution of master data across the enterprise, ensuring consistency and quality.
References:
* DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.
* The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball and Margy Ross.
NEW QUESTION # 22
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. Requiring only delta loads of changed data attributes
- C. Data format and transfer flexibility
- D. Source system conformance to a single standard data input format
- E. Subscriber conformance to a single standard data output format
Answer: C
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 # 23
Reference Data Dictionaries are authoritative listings of:
- A. Master Data sources
- B. External sources of data
- C. Master Data entities
- D. Semantic rules
- E. Master Data systems of record
Answer: B
Explanation:
* 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.
* Explanation:
* 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.
References:
* 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.
NEW QUESTION # 24
What characteristics does Reference data have that distinguish it from Master Data?
- A. It is less volatile, less complex, and typically smaller than Master Data sets
- B. It provides data for transactions
- C. It is always data from an outside source such as a governing body
- D. It is more volatile and needs to be highly structured
- E. It always has foreign database keys to link it to other data
Answer: E
Explanation:
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.
* 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 # 25
Bringing order to your Master Data would solve what?
- A. 60-80% of the most critical data quality problems
- B. Distributing data across the enterprise
- C. The need for a metadata repository
- D. Provide a place to store technical data elements
- E. 20 40% of the need to buy new servers
Answer: A
Explanation:
* Definitions and Context:
* Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization's official shared master data assets.
* Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.
* Explanation:
* Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization.
* Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
* Gartner Research, "The Impact of Master Data Management on Data Quality."
NEW QUESTION # 26
Business entities are represented by entity instances:
- A. In the form technical capabilities
- B. In the form of domains
- C. In the form of files
- D. In the form of business capabilities
- E. in the form of data/records
Answer: E
Explanation:
Business entities are represented within an organization through various forms, primarily as data or records within information systems.
* Technical Capabilities:
* While technical capabilities support the management and usage of business entities, they are not the representation of the entities themselves.
* Business Capabilities:
* Business capabilities describe the functions and processes that an organization can perform, but they do not represent individual business entities.
* Files:
* Files can contain data or records, but they are not the direct representation of business entities.
* Data/Records:
* Business entities are captured and managed as data or records within databases and information systems.
* These records contain the attributes and details necessary to uniquely identify and describe each business entity.
* Domains:
* Domains refer to specific areas of knowledge or activity but are not the direct representation of business entities.
NEW QUESTION # 27
Management of Reference and Master data is aimed to reduce cost and risk of having disparate data mainly caused by:
- A. Organicgrowth of systems and data, isolated systems, mergers and acquisitions
- B. High number of legacy applications and lack of expertise to evolve or replace them
- C. Migration to new technology platforms and evolution of landscape
- D. Lack of appropriate processes to assure data availability and accuracy
- E. Poor or non-existent data documentation available for developers and business analysts
Answer: A
Explanation:
Management of Reference and Master Data aims to mitigate the challenges of disparate data, which typically arise from:
* Organic Growth:
* Unplanned Expansion: Over time, organizations often develop new systems and applications organically, leading to isolated and redundant data stores.
* Inconsistent Data: These disparate systems often result in inconsistent and unreliable data.
* Isolated Systems:
* Siloed Applications: Independent systems that do not communicate effectively with each other can lead to multiple versions of the same data.
* Lack of Integration: Without proper integration, data consistency and quality suffer.
* Mergers and Acquisitions:
* Combining Systems: Mergers and acquisitions introduce the challenge of integrating different data systems and standards.
* Data Redundancy: Newly acquired systems often come with their own data sets, leading to redundancy and conflicts.
* 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 # 28
The following is a technique thatyou can find useful when implementing your Reference and Master program:
- A. None of the answers is correct
- B. Root Cause Analysis
- C. Process Management
- D. Extract Transformation Load (ETL)
- E. Business key cross references
Answer: E
Explanation:
When implementing a Reference and Master Data Management (RMDM) program, it is crucial to utilize techniques that ensure consistency, accuracy, and reliability of data across various systems. Business key cross-references is one such technique. This technique involves creating a mapping between different identifiers (keys) used across systems to represent the same business entity. This mapping ensures that data can be accurately and consistently referenced, integrated, and analyzed across different systems.
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, which emphasizes the importance of business key cross-referencing in MDM.
NEW QUESTION # 29
MDM is a lifecycle management process that includes the following activities with the exception of which activity?
- A. Ensuring effective and efficient retrieval and use of data and information by ETL logic
- B. Identifying improperly matched or merged instances and ensuring they are resolved and correctly associated with identifiers
- C. Enforcing the use of Master Data values within the organization
- D. Provisioning of access to trusted data across applications, either through direct reads, data services, or by replication feeds to transactional, warehousing or analytical data stores
- E. Identifying multiple instances of the same entity represented within and across data sources: building and maintaining identifiers and cross-references to enable information integration
Answer: A
Explanation:
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.
NEW QUESTION # 30
For MDMs. what is meant by a classification scheme?
- A. Codes that represent a controlled set of values
- B. A vocabulary view covering a limited range of topics
- C. A way of classifying unstructured data
- D. Descriptive language used to control objects
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
The biggest challenge to implementing Master Data Management will be:
- A. the disparity between sources
- B. Indexes and foreign keys
- C. The inability to get the DBAs to provide their table structures
- D. Complex queries
- E. Defining requirements for master data within an application
Answer: A
Explanation:
Implementing Master Data Management (MDM) involves several challenges, but the disparity between data sources is often the most significant.
* Disparity Between Sources:
* Different systems and applications often store data in varied formats, structures, and standards, leading to inconsistencies and conflicts.
* Data integration from disparate sources requires extensive data cleansing, normalization, and harmonization to create a single, unified view of master data entities.
* Data Quality Issues:
* Variability in data quality across sources can further complicate the integration process.
Inconsistent or inaccurate data must be identified and corrected.
* Defining Requirements for Master Data:
* While defining requirements is crucial, it is typically a manageable step through collaboration with business and technical stakeholders.
* DBA Cooperation:
* Getting Database Administrators (DBAs) to share table structures can pose challenges, but it is not as critical as dealing with disparate data sources.
* Complex Queries and Indexes:
* While important for performance optimization, complex queries and indexing issues are more technical hurdles that can be resolved with appropriate database management practices.
NEW QUESTION # 32
Which of the following Is a characteristic of a probabilistic matching algorithm?
- A. A score is assigned based on weight and degree of match
- B. All answers are correct
- C. Following the matching process there are typically records requiring manual review and decisioning.
- D. Each variable to be matched is assigned a weight based on its discriminating power
- E. Individual attribute matching scores arc used to create a match probability percentage.
Answer: B
Explanation:
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.
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 # 33
MDM matching algorithms benefit from all of the following data characteristics except for which of the following?
- A. High validity of the data
- B. Structural heterogeneity of data elements
- C. Distinctiveness across the population of data
- D. Low number of common data points
- E. High level of comparability of the data elements
Answer: B
Explanation:
MDM matching algorithms benefit from various data characteristics but do not benefit from "Structural heterogeneity of data elements."
* Matching Algorithms:These are used in MDM to identify and link data records that refer to the same entity across different systems.
* Data Characteristics:
* Distinctiveness:Helps in accurately matching records.
* Common Data Points:Aids in the comparison process.
* Comparability:Facilitates effective matching.
* Validity:Ensures the data is accurate and reliable.
* Structural Heterogeneity:Different structures can complicate the matching process, making it harder to align data.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide
NEW QUESTION # 34
These are two metrics you must produce totrackthe effectiveness of your Reference and Master Data Program:
- A. Data model Validation and Measurement
- B. Value and sustainability
- C. Data Quality and Security Incident Metrics
- D. Data Quality and Data Consumption Trends in implementation and Access Control
Answer: D
Explanation:
Tracking the effectiveness of a Reference and Master Data Management (RMDM) program requires monitoring various metrics that reflect the quality, usage, and governance of the data.The key metrics in this context are Data Quality and Data Consumption Trends, along with Access Control.
* Data Quality:
* Data quality metrics assess the accuracy, completeness, consistency, and reliability of the master and reference data.
* Common data quality metrics include:
* Accuracy:Correctness of data values.
* Completeness:Presence of all required data values.
* Consistency:Uniformity of data across different systems.
* Timeliness:Up-to-date and current data.
* Tracking data quality helps identify issues and areas for improvement, ensuring that the data remains fit for purpose.
* Data Consumption Trends:
* Monitoring data consumption trends involves analyzing how data is used across the organization.
* This includes tracking the frequency and volume of data access, the number of users accessing the data, and the business processes that depend on the data.
* Understanding consumption trends helps in identifying critical data assets, optimizing data delivery, and ensuring that the data meets the needs of its users.
* Access Control:
* Access control metrics track the security and governance of master and reference data.
* This includes monitoring who has access to the data, how the data is accessed, and any unauthorized access attempts.
* Ensuring proper access control is crucial for data security and compliance with regulatory requirements.
* Value and Sustainability:
* While important, these metrics focus more on the overall value and long-term viability of the RMDM program rather than specific operational effectiveness.
NEW QUESTION # 35
Which of the following isNOT part of MDM Lifecycle Management?
- A. Identifying improperly matched or merged instances of data
- B. Identifying multiple instances of the same entity
- C. Maintaining cross-references to enable information integration
- D. Reconciling and consolidating data
- E. Establishing recovery and backup rules
Answer: E
Explanation:
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.
NEW QUESTION # 36
The most difficult MDM style to implement data governance is which of following-
- A. Coexistence style
- B. Registry style
- C. Centralized style
- D. Linkage style
- E. Consolidation style
Answer: B
Explanation:
The registry style is the most difficult MDM style to implement data governance due to its reliance on maintaining a central registry of master data without consolidating data physically. This method makes it challenging to ensure consistent governance across disparate systems since data remains distributed and only loosely connected via the registry.
References:
* DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.
* Master Data Management: Creating a Single Source of Truth by David Loshin.
NEW QUESTION # 37
The easiest MDM style to implement data governance based on controls that can be placed on persistent data is:
- A. Centralized style
- B. Registry style
- C. Agile Style
- D. Multi-hub
- E. Consolidation style
Answer: A
Explanation:
The centralized style is the easiest MDM style to implement data governance because it consolidates all master data into a single central repository. This centralization simplifies the application of data governance controls, ensuring consistent data quality, standards, and policies are applied across the organization.
References:
* DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.
* Master Data Management and Data Governance by Alex Berson and Larry Dubov.
NEW QUESTION # 38
Why would a company not develop Master Data?
- A. The process is too disruptive
- B. Data Quality is not a priority.
- C. Fail to sec value in integrating their data
- D. Lack of commitment
- E. All of these are correct
Answer: E
Explanation:
Several factors can deter a company from developing a master data program, including the perceived value, commitment level, disruption, and data quality priorities.
* Fail to See Value in Integrating Their Data:
* If a company does not recognize the benefits of integrating and managing master data, it may not invest in an MDM program.
* Lack of Commitment:
* Developing an effective MDM program requires long-term commitment from leadership and stakeholders. Without this commitment, the program is unlikely to succeed.
* The Process is Too Disruptive:
* Implementing an MDM program can be disruptive to existing processes and systems. The perceived disruption can deter companies from pursuing it.
* Data Quality is Not a Priority:
* If a company does not prioritize data quality, it may not see the need for a robust MDM program.
Poor data quality can undermine the effectiveness of business processes and decision-making.
NEW QUESTION # 39
Within the Corporate Information Factory, what data is used to understand transactions?
- A. Security Data and Master Data
- B. Reference Data and Vendor Data
- C. Master Data and Unstructured Data
- D. Master Data. Reference Data, and External Data
- E. Internal Data. Physical Schemas
Answer: D
Explanation:
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.
References:
* 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.
NEW QUESTION # 40
Data Integration tor MDM and Reference data should:
- A. Have one only one value for the same concept
- B. Ignore minor changes because they will disrupt the entire system
- C. Not allow ad-hoc changes to the data
- D. Perform root analysis of data lineage at the time of integration
- E. Be designed to ensure timely extraction and distribution of data across the enterprise
Answer: E
Explanation:
Data integration for Master Data Management (MDM) and reference data is a critical process that ensures data consistency, accuracy, and availability across the enterprise. The goal is to enable seamless data flow and access for various business functions.
* Timely Extraction and Distribution:
* Data integration processes must be designed to extract and distribute data efficiently and in a timely manner to ensure that all parts of the organization have access to up-to-date information.
* This involves implementing data pipelines and ETL (Extract, Transform, Load) processes that can handle large volumes of data and deliver it where needed without delays.
* Root Analysis of Data Lineage:
* While important for understanding data origins and transformations, root analysis of data lineage is typically part of data governance and auditing processes, not a primary focus during real-time integration.
* Ad-Hoc Changes:
* While controlled environments are important, integration processes should be flexible enough to accommodate necessary changes without compromising data integrity.
* Single Value for the Same Concept:
* Ensuring a single source of truth is essential but requires robust data governance and harmonization efforts rather than just focusing on integration.
* Ignoring Minor Changes:
* Ignoring changes can lead to data quality issues and discrepancies. Effective data integration should handle changes efficiently without causing disruptions.
NEW QUESTION # 41
Which of the following best describes Mister Data?
- A. Master Data is data thatis mastered by business users
- B. Master Data is data about business entities that provide visibility into organizational functions
- C. Master Data is data about technical entities that provide context for transactions
- D. Master Data is another name for Reference Data
- E. Master Data is data about business entities that provide context for business transactions and analysis
Answer: E
Explanation:
Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.
* Data about Business Entities:
* Master data typically includes key entities such as customers, products, suppliers, employees, and locations.
* These entities are fundamental to business operations and provide the necessary context for transactions and analysis.
* Providing Context for Business Transactions:
* Master data provides the foundational information required to conduct business transactions.
* For example, customer master data is used in sales transactions, while product master data is used in inventory management.
* Supporting Business Analysis:
* Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.
* It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.
* Other Options:
* A: Master data and reference data are distinct; reference data is used to categorize master data.
* B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.
* C: Provides visibility but also context for transactions and analysis.
* E: Master data is about business entities, not technical entities.
NEW QUESTION # 42
A division of power approach to master data governance provides the benefit of:
- A. Better alignment of decisions based on varying levels of organizational data sharing
- B. Facilitating a decision by committee model
- C. Centralizing responsibility
- D. Lower expense
- E. Spreads the blame for bad decisions
Answer: A
Explanation:
* Division of Power in Data Governance:This approach distributes decision-making authority across different levels or areas within the organization.
* Benefits:
* Better alignment of decisions:By distributing power, decisions can be made that are better suited to the specific needs and contexts of different parts of the organization. This ensures that decisions about data management are relevant and effective for each particular area.
* Avoids centralization issues:Centralized decision-making can often be disconnected from the needs of different departments or functions.
* Improved responsiveness:
Decentralized governance can enable faster and more contextually appropriate responses to data management issues.
* Other Options Analysis:
* Spreads the blame for bad decisions:This is not a strategic benefit but rather a negative consequence.
* Centralizing responsibility:This contradicts the concept of division of power.
* Lower expense:While decentralization might lead to better decision-making, it doesn't inherently mean lower costs.
* Facilitating a decision by committee model:This can lead to slower decision-making processes and isn't the primary benefit of a division of power.
* Conclusion:The key benefit of a division of power approach in master data governance is the better alignment of decisions based on varying levels of organizational data sharing.
References:
* DMBOK Guide, sections on Data Governance and Organizational Structures.
* CDMP Examination Study Materials.
NEW QUESTION # 43
What technology option can provide better support of a Registry style MDM?
- A. JSON mapping
- B. Columnar database
- C. Data virtualization
- D. ETL toolset
- E. Complex queries
Answer: C
Explanation:
Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.
* JSON Mapping:
* JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.
* ETL Toolset:
* ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.
* Columnar Database:
* Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.
* Complex Queries:
* While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.
* Data Virtualization:
* Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.
* It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.
NEW QUESTION # 44
What role would you expect Data Governance to play in the development of an enterprise wide MDM strategy?
- A. Helping the DBAs design efficient database tables
- B. Identify data sources to be integrated
- C. Producing and managing an enterprise conceptual data model to focus and support the MDM strategy
- D. Developing xml for data messaging.
- E. Identify different approaches to data processing.
Answer: C
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 # 45
Which of the following is true about MDM?
- A. A MDMprogram must include a MDM software application
- B. MDM programs have a definitive life span
- C. Manages master data formally with a high degree of diligence and collaboration
- D. Once master data is published by a MDM hub. it no longer is considered master ' data
- E. Master data is not managed without a formal MDM program
Answer: C
Explanation:
MDM (Master Data Management) is characterized by formal management with a high degree of diligence and collaboration. Here's why:
* Formal Management:
* Structured Processes: MDM involves structured processes for managing master data, including data governance, data quality management, and data stewardship.
* Policies and Standards: Establishes and enforces policies and standards to ensure data consistency, accuracy, and integrity.
* Collaboration:
* Cross-Functional Teams: Requires collaboration across different departments, including IT, business units, and data governance teams.
* Stakeholder Involvement: Engages various stakeholders in the data management process, ensuring that master data meets the needs of the entire 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)"
NEW QUESTION # 46
......
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