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| Section | Objectives |
|---|
| Topic 1: Data Management Fundamentals | - Data Ethics
- 1. Ethical principles and practices
- 2. Privacy and consumer protection
- 3. Responsible AI and algorithmic bias
- Metadata Management
- 1. Metadata types and definitions
- 2. Metadata standards and integration
- 3. Metadata architecture and repository
- Data Business and Data Management
- 1. Business drivers and data roles
- 2. Data management frameworks
- 3. Data management definitions and principles
- Data Architecture
- 1. Data modeling and design
- 2. Architecture frameworks and tools
- 3. Enterprise data architecture concepts
- Data Integration and Interoperability
- 1. ETL/ELT processes
- 2. Data interoperability standards
- 3. Data integration techniques
- Content and Document Management
- 1. Records management
- 2. Unstructured data management
- 3. Content lifecycle management
- Data Storage and Operations
- 1. Database architecture and technology
- 2. Data storage design principles
- 3. Database security and access control
- Data Security
- 1. Privacy regulations compliance
- 2. Data classification and handling
- 3. Security frameworks and controls
- Master Data and Reference Data
- 1. Reference data management
- 2. MDM and RDM concepts
- 3. Master data management processes
- Data Strategy
- 1. Strategic planning process
- 2. Data management strategy development
- 3. Data governance strategy
- Data Modeling and Design
- 1. Data modeling techniques
- 2. Normalization and denormalization
- 3. Conceptual, logical, and physical models
- Data Risk Management
- 1. Risk identification and assessment
- 2. Data protection and compliance
- 3. Risk mitigation strategies
- Data Warehousing and Business Intelligence
- 1. BI and analytics fundamentals
- 2. Reporting and visualization
- 3. Data warehousing concepts and architectures
- Data Governance
- 1. Governance bodies and organizational roles
- 2. Policies, standards, and procedures
- 3. Governance framework and scope
- 4. Data quality management
- Data Quality
- 1. Data quality measurement and improvement
- 2. Data profiling and cleansing
- 3. Data quality dimensions
|
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DAMA Data Management Fundamentals Sample Questions (Q220-Q225):
NEW QUESTION # 220
Data replication has two dimensions of scaling: diagonal and lateral
Answer: A
NEW QUESTION # 221
A goal of reference and master data management is for data to ensure shared data is:
- A. Secure, auditable, publicly available and free
- B. Secure, auditable. complete and relevant
- C. Continuous, consistent, current and private
- D. Complete, consistent, content and relevant
- E. Complete, consistent, current and authoritative
Answer: E
NEW QUESTION # 222
Data Governance focuses exclusively on:
- A. Alignment of IT strategies and investments with enterprise goals and strategies
- B. Decisions about IT investments
- C. Rules and regulations including GDPR
- D. The COBIT (Control Objectives for Information and Related Technology) framework
- E. The management of data assets and of data as an asset
Answer: E
NEW QUESTION # 223
The Shewhart chart contains the following elements:
- A. Do
- B. Check
- C. Utilization
- D. All of the above
- E. Plan
- F. Act
Answer: A,B,E,F
NEW QUESTION # 224
Which of the following is a Data Quality principle?
- A. Criticality
- B. Prevention
- C. Governance
- D. All of these
- E. Standards Driven
Answer: D
NEW QUESTION # 225
......
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