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質問 # 52
Information Governance is a concept that covers the 'what', how', and why' pertaining to the data assets of an organization. The 'what', 'how', and 'why' are respectively handled by the following functional areas:
- A. Customer Experience. Information Security, and data Governance
- B. Data Management. Information Technology, and Compliance
- C. Data Management, Information Security, and Customer Experience
- D. Data Governance. Information Technology, and Customer Experience
- E. Data Governance. Information Security, and Compliance
正解：E
解説：
Information Governance involves managing and controlling the data assets of an organization, addressing the
'what', 'how', and 'why'.
* 'What' pertains to Data Governance, which defines policies and procedures for data management.
* 'How' relates to Information Security, ensuring that data is protected and secure.
* 'Why' is about Compliance, ensuring that data management practices meet legal and regulatory requirements.
References:
* DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 1: Data Governance.
* "Information Governance: Concepts, Strategies, and Best Practices" by Robert F. Smallwood.
質問 # 53
Master and Reference Data are forms of:
- A. Data Mapping
- B. Data Integration
- C. Data Security
- D. Data Quality
- E. Data Architecture
正解：E
解説：
Master and Reference Data are forms of Data Architecture. Here's why:
* Data Architecture Definition:
* Structure and Design: Data architecture involves the structure and design of data systems, including how data is organized, stored, and accessed.
* Components: Encompasses various components, including data models, data management processes, and data governance frameworks.
* Role of Master and Reference Data:
* Core Components: Master and Reference Data are integral components of an organization's data architecture, providing foundational data elements used across multiple systems and processes.
* Organization and Integration: They play a critical role in organizing and integrating data, ensuring consistency and accuracy.
* 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)"
質問 # 54
The following are examples of entities for which you need to manage master data:
- A. Customer, Product,Employee
- B. Product, Order,Inventory
- C. Employee Assignment. Employer. Transaction
- D. Party, Account Balance,Order
- E. Customer, Transaction,Product
正解：A
解説：
Entities such as Customer, Product, and Employee are typical examples of master data that need to be managed.
* Master Data Entities:These are the key data objects around which business transactions are conducted.
* Examples:
* Customer:Central to sales and service operations.
* Product:Essential for inventory and sales management.
* Employee:Critical for HR and payroll systems.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide
質問 # 55
MOM is most accurately and comprehensively defined in which of the following definitions?
- A. Processes that maintain master data
- B. The creation of a single instance of an attribute across the enterprise as the version of the truth
- C. A technology foundation for the management of key business entities
- D. Governed processes enabled by people and technologies providing Master Data that is understood, trusted, controlled, and fit-for-purpose
- E. The integration of systems of record that can be leveraged by a governance program
正解：D
解説：
Master Data Management (MDM) involves various processes and technologies to ensure that master data is accurate, consistent, and trustworthy. The most comprehensive definition of MDM captures its multi-faceted nature, encompassing governance, technology, and organizational roles.
* Governed Processes:
* MDM involves establishing governance processes to define policies, standards, and procedures for managing master data.
* These processes ensure that data is handled consistently and according to defined rules.
* Role of People and Technologies:
* Effective MDM requires the involvement of people, including data stewards, data owners, and governance committees, who are responsible for overseeing and managing master data.
* Technologies, such as MDM software and tools, facilitate the implementation of governance processes, data integration, data quality management, and synchronization.
* Key Objectives:
* Master data should be understood by stakeholders, ensuring clarity and common understanding of data definitions and attributes.
* Trust in master data is achieved through rigorous data quality and governance practices.
* Data should be controlled, meaning that access, usage, and changes to the data are managed and monitored.
* Master data must be fit-for-purpose, meeting the specific needs and requirements of the organization's business processes.
質問 # 56
What is a Canonical Data Model?
- A. An alternate data structure which is less costly to the organization
- B. Used for data replication
- C. A common data modelwhich standardizes the format in which data will be shared
- D. Data that is not used frequently is moved to the Canonical model
- E. Used in a transactional operating environment
正解：C
解説：
A Canonical Data Model (CDM) is a design pattern used in data integration and exchange to create a standardized format for data that will be shared across different systems.
* Canonical Data Model Definition:
* A CDM provides a common, consistent, and agreed-upon representation of data across different systems within an organization.
* It serves as an intermediary format to simplify data exchange and integration.
* Standardization:
* The primary purpose of a CDM is to standardize the format of data. This ensures that data from different sources can be integrated, understood, and used without needing extensive transformation or reformatting.
* It addresses the problem of data heterogeneity, where different systems may have their own data formats and structures.
* Data Integration:
* By using a CDM, organizations can facilitate easier and more efficient data integration processes.
Data from various systems can be mapped to the canonical model, enabling seamless data exchange and interoperability.
* It simplifies the maintenance and management of data integration solutions by providing a single, unified data model.
* Use Cases:
* CDMs are commonly used in enterprise application integration (EAI), service-oriented architectures (SOA), and other environments where data needs to be exchanged between multiple systems.
質問 # 57
Which of the following are reasons why marketing is important to a MDM program?
- A. Helps to grow the base of subscribers
- B. Promotes benefits to leadership
- C. Helps ensure long-term sustainability of program
- D. Encourages sources to onboard their master data to MDM Inventory
- E. All of the above
正解：E
解説：
Marketing is crucial to the success of a Master Data Management (MDM) program for several reasons:
* Encouraging Onboarding:
* Master Data Onboarding: Effective marketing strategies can encourage different data sources to integrate their master data into the MDM system, ensuring comprehensive data coverage.
* Promoting Benefits to Leadership:
* Leadership Buy-in: Marketing the benefits of MDM to organizational leadership can secure necessary support and resources for the program. Highlighting efficiencies, cost savings, and improved decision-making can be persuasive.
* Growing Subscriber Base:
* User Engagement: Promoting the MDM program can help grow the base of subscribers and users who rely on the master data, ensuring the data is used effectively across the organization.
* Ensuring Long-term Sustainability:
* Sustainability: Continuous marketing helps maintain interest and investment in the MDM program, ensuring its long-term sustainability and relevance within 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)"
質問 # 58
What is the best way to ensure you have high quality reference data?
- A. Only use data from external data providers
- B. Implement Data Governance and Stewardship
- C. Create drop-down menus for data entry to prevent all invalid data
- D. Only use reference data from government sources
- E. Only use standard reference data provided by ISO
正解：B
解説：
Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.
* Government Sources:
* While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.
* Drop-Down Menus:
* Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.
* Data Governance and Stewardship:
* Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.
* Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.
* Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.
* Standard Reference Data (ISO):
* Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.
* External Data Providers:
* External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.
質問 # 59
Which of the following is NOT ,1 characteristic of n deterministic matching algorithm?
- A. Is not highly dependent on the quality of the data being matched
- B. Is better suited when there is no great consequence to an error in matching
- C. Matches exact character to character of one or more fields
- D. All identifiersbeing matched have equal weight
- E. Has a discrete all or nothing outcome
正解：A
解説：
Deterministic matching algorithms rely on exact matches between data fields to determine if records are the same. These algorithms require high-quality data because any discrepancy, such as typographical errors or variations in data entry, can prevent a match.
Characteristics of deterministic matching:
* It has a discrete all or nothing outcome (C).
* It matches exact character to character of one or more fields (D).
* All identifiers being matched have equal weight (E).
Since deterministic matching is highly dependent on the quality of the data being matched, option B is incorrect.
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.
質問 # 60
Managing Master Data involves:
- A. Managing database keys
- B. Managing structured and unstructured data
- C. Managing process models
- D. Managing security risks
- E. Managing transaction data
正解：B
解説：
Managing Master Data involves several key activities, primarily focusing on:
* Structured and Unstructured Data:
* Structured Data: Managing well-defined data types, such as relational databases, where data is organized into tables and fields.
* Unstructured Data: Handling data that does not have a predefined format or structure, such as emails, documents, and multimedia files.
* Comprehensive Management:
* Data Integration: Ensuring that data from various sources, both structured and unstructured, is integrated into the master data repository.
* Data Quality: Implementing processes and tools to maintain high data quality for both structured and unstructured data.
* 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)"
質問 # 61
These are two metrics you must produce totrackthe effectiveness of your Reference and Master Data Program:
- A. Data Quality and Data Consumption Trends in implementation and Access Control
- B. Value and sustainability
- C. Data model Validation and Measurement
- D. Data Quality and Security Incident Metrics
正解：A
解説：
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.
質問 # 62
The following is a technique thatyou can find useful when implementing your Reference and Master program:
- A. Extract Transformation Load (ETL)
- B. Process Management
- C. Root Cause Analysis
- D. Business key cross references
- E. None of the answers is correct
正解：D
解説：
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.
質問 # 63
The format and allowable ranges of Master Data values are dictated by:
- A. Engagement rules
- B. Database limitations
- C. Processing rules
- D. Semantic rules
- E. Business rules
正解：E
解説：
The format and allowable ranges of Master Data values are primarily dictated by business rules.
* Business Rules:
* Business rules define the constraints, formats, and permissible values for master data based on the organization's operational and regulatory requirements.
* These rules ensure that data conforms to the standards and requirements necessary for effective business operations.
* Semantic Rules:
* These rules pertain to the meaning and context of the data but do not directly dictate formats and ranges.
* Processing Rules:
* These rules focus on how data is processed but not on the allowable values or formats.
* Engagement Rules:
* These rules govern interactions and workflows rather than data formats and ranges.
* Database Limitations:
* While database limitations can impose constraints, they are typically secondary to the business rules that drive data requirements.
質問 # 64
The Reference Data Change Request Process does NOT include which of the following subprocesses:
- A. Identify Stakeholder
- B. Decide and Communicate
- C. Monitor Database Change
- D. Receive Change Request
- E. Identify Impact
正解：C
解説：
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)"
質問 # 65
The ISO definition of Master Data quality is which of the following?
- A. The degree to which the data's characteristics fulfill individual users' requirements
- B. Identifies the company that created and owns the Master Data
- C. Data meets the objective dimensions but not the subjective dimensions
- D. Data meets all common requirements of all data users
- E. Data is compliant to all international, country, and industry standards
正解：A
解説：
The ISO definition of Master Data quality focuses on the degree to which the data's characteristics meet the requirements of individual users. This implies that quality is subjective and depends on whether the data is suitable and adequate for its intended purpose, fulfilling the specific needs of its users.
References:
* ISO 8000-8:2015 - Data quality - Part 8: Information and data quality: Concepts and measuring.
* DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 13: Data Quality Management.
質問 # 66
Which is NOT considered a type of Master Data relationship?
- A. Grouping based on common criteria
- B. Survivorship
- C. Fixed-Level Hierarchy
- D. Customer Household
- E. Ragged-Level Hierarchy
正解：B
解説：
Master Data relationships define how different master data entities are related to each other within an organization. These relationships are crucial for understanding and managing the dataeffectively. The types of master data relationships generally include hierarchies, groupings, and associations that help in organizing and categorizing the data.
* Customer Household:
* This refers to grouping individual customers into a single household entity. It is commonly used in consumer industries to understand the relationships and dynamics within a household.
* Fixed-Level Hierarchy:
* A hierarchy with a predetermined number of levels. Each level has a specific position and relationship to other levels, such as organizational hierarchies or product categorization.
* Ragged-Level Hierarchy:
* Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.
* Grouping based on common criteria:
* This involves creating groups or segments of data based on shared attributes or criteria. For example, grouping products by category or customers by region.
* Survivorship (NOT a relationship):
* Survivorship pertains to the process of determining the most accurate and relevant data when multiple records exist for the same entity. It is a data quality and management process, not a type of relationship.
質問 # 67
Management of Reference and Master data is aimed to reduce cost and risk of having disparate data mainly caused by:
- A. Migration to new technology platforms and evolution of landscape
- B. Poor or non-existent data documentation available for developers and business analysts
- C. High number of legacy applications and lack of expertise to evolve or replace them
- D. Lack of appropriate processes to assure data availability and accuracy
- E. Organicgrowth of systems and data, isolated systems, mergers and acquisitions
正解：E
解説：
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)"
質問 # 68
Which of the following is NOT a metric that c.tn be tied to Reference and Master Data Quality?
- A. Data sharing volume
- B. The rate of change of data values
- C. Service Level Agreements
- D. Data sharing usage
- E. Operational functions
正解：E
解説：
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)"
質問 # 69
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
正解：B
解説：
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)"
質問 # 70
The concept of tracking the number of MDM subject areas and source system attributes Is referred to as:
- A. Publish and Subscribe
- B. Subject Area and Attribute Scope and Coverage
- C. Mapping and Integration
- D. Hub and Spoke
正解：B
解説：
Tracking the number of MDM subject areas and source system attributes refers to defining the scope and coverage of the subject areas and attributes involved in an MDM initiative. This process includes identifying all the data entities (subject areas) and the specific attributes (data elements) within those entities that need to be managed across the organization. By establishing a clear scope and coverage, organizations can ensure that all relevant data is accounted for and appropriately managed.
References:
* DAMA-DMBOK2 Guide: Chapter 10 - Master and Reference Data Management
* "Master Data Management and Data Governance" by Alex Berson, Larry Dubov
質問 # 71
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