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| Section | Objectives |
|---|
| Topic 1: Data Management Fundamentals | - Master Data and Reference Data
- 1. MDM and RDM concepts
- 2. Master data management processes
- 3. Reference data management
- Data Ethics
- 1. Ethical principles and practices
- 2. Responsible AI and algorithmic bias
- 3. Privacy and consumer protection
- Data Warehousing and Business Intelligence
- 1. Data warehousing concepts and architectures
- 2. BI and analytics fundamentals
- 3. Reporting and visualization
- Data Risk Management
- 1. Risk identification and assessment
- 2. Risk mitigation strategies
- 3. Data protection and compliance
- Data Governance
- 1. Governance framework and scope
- 2. Data quality management
- 3. Governance bodies and organizational roles
- 4. Policies, standards, and procedures
- Content and Document Management
- 1. Content lifecycle management
- 2. Unstructured data management
- 3. Records management
- Data Business and Data Management
- 1. Data management frameworks
- 2. Business drivers and data roles
- 3. Data management definitions and principles
- Data Storage and Operations
- 1. Database security and access control
- 2. Database architecture and technology
- 3. Data storage design principles
- Data Security
- 1. Security frameworks and controls
- 2. Data classification and handling
- 3. Privacy regulations compliance
- Data Strategy
- 1. Data governance strategy
- 2. Strategic planning process
- 3. Data management strategy development
- Data Integration and Interoperability
- 1. ETL/ELT processes
- 2. Data interoperability standards
- 3. Data integration techniques
- Metadata Management
- 1. Metadata types and definitions
- 2. Metadata architecture and repository
- 3. Metadata standards and integration
- Data Quality
- 1. Data quality measurement and improvement
- 2. Data profiling and cleansing
- 3. Data quality dimensions
- Data Modeling and Design
- 1. Normalization and denormalization
- 2. Conceptual, logical, and physical models
- 3. Data modeling techniques
- Data Architecture
- 1. Enterprise data architecture concepts
- 2. Data modeling and design
- 3. Architecture frameworks and tools
|
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DAMA Data Management Fundamentals Sample Questions (Q473-Q478):
NEW QUESTION # 473
Data access control can be organized at an individual level or group level, depending on the need.
Answer: A
NEW QUESTION # 474
When developing a Data Governance operating framework, what areas should be considered?
- A. Data management skills, budget considerations & organization strategy
- B. Cultural factors, the role of a data steward, & third-party data policies
- C. Impacts of regulations, the value of data to the organization, data modelling skills, & third-party data policies
- D. A model that documents the business, cultural factors, the impacts of regulations, and the value of data to the organization
- E. Data inputs, context diagrams, methods and procedures
Answer: D
NEW QUESTION # 475
A deliverable in the data security context diagram is the data security architecture.
Answer: A
NEW QUESTION # 476
A goal of metadata management is to manage data related business terminology in order toc
- A. Successfully size the database
- B. Ensure the business processes align to the data model
- C. Ensure people understand data content and can use data consistently
- D. Ensure accurate data requirements are gathered for reporting
- E. Ensure people understand data definition in Bl systems
Answer: C
NEW QUESTION # 477
Where does the ethical responsibility lie with respect to managing data to reduce risks of misrepresentation, misuse, or misunderstanding?
- A. Business owns the data and is therefore ethically responsible for managing data across the data lifecycle
- B. Data modellers carry responsibility to ensure that data handling ethics are achieved by design
- C. Everybody that makes use of data within the organization is responsible for the ethical use of the data
- D. The ethical responsibility lies with Data Management professionals to manage data and to manage the associated risks
- E. Risk Management is responsible for ethical data management
Answer: C
Explanation:
Ethical responsibility for data is shared across the organization, as all users must adhere to ethical principles.
The DAMA-DMBOK states: "Ethical data management is a collective responsibility, where everyone who uses or handles data within the organization is accountable for ensuring its ethical use to prevent misrepresentation, misuse, or misunderstanding" (DMBOK2, Chapter 14: Data Handling Ethics, p. 561).
Options A, C, D, and E assign responsibility too narrowly to specific roles or groups.
Reference:DAMA-DMBOK2, Chapter 14: Data Handling Ethics, Section 3.1, p. 561.
NEW QUESTION # 478
......
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