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DAMA DMF-1220 Exam Syllabus Topics:

Section	Objectives
Data Management Fundamentals	- Content and Document Management 1. Unstructured data management 2. Content lifecycle management 3. Records management - Data Risk Management 1. Risk mitigation strategies 2. Data protection and compliance 3. Risk identification and assessment - Metadata Management 1. Metadata standards and integration 2. Metadata architecture and repository 3. Metadata types and definitions - Master Data and Reference Data 1. Reference data management 2. Master data management processes 3. MDM and RDM concepts - Data Security 1. Privacy regulations compliance 2. Security frameworks and controls 3. Data classification and handling - Data Architecture 1. Data modeling and design 2. Architecture frameworks and tools 3. Enterprise data architecture concepts - Data Storage and Operations 1. Database security and access control 2. Data storage design principles 3. Database architecture and technology - Data Business and Data Management 1. Business drivers and data roles 2. Data management frameworks 3. Data management definitions and principles - Data Ethics 1. Privacy and consumer protection 2. Responsible AI and algorithmic bias 3. Ethical principles and practices - Data Modeling and Design 1. Normalization and denormalization 2. Data modeling techniques 3. Conceptual, logical, and physical models - Data Integration and Interoperability 1. Data integration techniques 2. ETL/ELT processes 3. Data interoperability standards - Data Strategy 1. Data governance strategy 2. Data management strategy development 3. Strategic planning process - Data Governance 1. Governance framework and scope 2. Data quality management 3. Policies, standards, and procedures 4. Governance bodies and organizational roles - Data Warehousing and Business Intelligence 1. Reporting and visualization 2. BI and analytics fundamentals 3. Data warehousing concepts and architectures - Data Quality 1. Data quality dimensions 2. Data quality measurement and improvement 3. Data profiling and cleansing

Section

Objectives

Data Management Fundamentals

- Content and Document Management

1. Unstructured data management
2. Content lifecycle management
3. Records management

- Data Risk Management

1. Risk mitigation strategies
2. Data protection and compliance
3. Risk identification and assessment

- Metadata Management

1. Metadata standards and integration
2. Metadata architecture and repository
3. Metadata types and definitions

- Master Data and Reference Data

1. Reference data management
2. Master data management processes
3. MDM and RDM concepts

- Data Security

1. Privacy regulations compliance
2. Security frameworks and controls
3. Data classification and handling

- Data Architecture

1. Data modeling and design
2. Architecture frameworks and tools
3. Enterprise data architecture concepts

- Data Storage and Operations

1. Database security and access control
2. Data storage design principles
3. Database architecture and technology

- Data Business and Data Management

1. Business drivers and data roles
2. Data management frameworks
3. Data management definitions and principles

- Data Ethics

1. Privacy and consumer protection
2. Responsible AI and algorithmic bias
3. Ethical principles and practices

- Data Modeling and Design

1. Normalization and denormalization
2. Data modeling techniques
3. Conceptual, logical, and physical models

- Data Integration and Interoperability

1. Data integration techniques
2. ETL/ELT processes
3. Data interoperability standards

- Data Strategy

1. Data governance strategy
2. Data management strategy development
3. Strategic planning process

- Data Governance

1. Governance framework and scope
2. Data quality management
3. Policies, standards, and procedures
4. Governance bodies and organizational roles

- Data Warehousing and Business Intelligence

1. Reporting and visualization
2. BI and analytics fundamentals
3. Data warehousing concepts and architectures

- Data Quality

1. Data quality dimensions
2. Data quality measurement and improvement
3. Data profiling and cleansing

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DAMA Data Management Fundamentals Sample Questions (Q50-Q55):

NEW QUESTION # 50
Communications are essential to the success of a DMM or Data Governance assessment. Communications are important because:

A. Findings may affect stakeholder's positions and roles
B. Each organization will approach data management and Data Governance communications differently
C. Many organizations will overlook the communications aspect of a data management or Data Governance assessment
D. Data management and Data Governance communications are different from each other
E. Communications can influence the socialization and acceptance of the results and implementation of a data management or Data Governance program

Answer: E

NEW QUESTION # 51
How can the Data Governance process best support Regulatory reporting requirements?

A. By creating a map of the enterprise data stores where copies of information may be found
B. By performing a data audit
C. By highlighting challenges of multiple definitions within the enterprise
D. By providing a look up service for definitions
E. By ensuring that data is properly owned, understood, defined, documented and controlled

Answer: E

NEW QUESTION # 52
Which of the following provides the strongest tangible reason for driving initiation of a Data Governance process in an enterprise?

A. Increasing regulation in industry can impose real penalties in terms of substantial fines or jail terms for non-compliance to properly managed data (e.g. accurate reporting/accounting)
B. There is typically significant waste in an enterprise through duplication of reports and metrics
C. There is disagreement among divisions on the meaning of key metrics
D. Each division of an enterprise may have its own way to describe its business and associated metrics
E. I ' ve been to a conference and found that lots of our competitors are doing it

Answer: B

NEW QUESTION # 53
A e-discovery readiness assessment should examine and identify opportunities for the commercial response program.

A. TRUE
B. FALSE

Answer: B

NEW QUESTION # 54
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:

A. Data modelling over data architecture
B. Security over enterprise data management
C. Data quality over master data management
D. Technology acquisition over a single point of truth
E. Processes over a single golden record

Answer: D

Explanation:
The correct answer is C. A proliferation of separate warehouses, lakes, and unmanaged "swamps" commonly signals a technology-first approach in which platforms are acquired independently rather than governed through a coherent enterprise data architecture. DAMA distinguishes managing data from managing information technology and describes a data-centric organization as one that treats data as an enterprise asset across its lifecycle. A single point of truth does not necessarily mean one physical database, but it does require authoritative definitions, governed sources, consistent metadata, and controlled integration. Multiple disconnected repositories increase duplication, inconsistency, lineage problems, and conflicting versions of business facts. Therefore, the strongest interpretation is that the organization is prioritizing technology acquisition over establishing a governed and authoritative view of enterprise data.

NEW QUESTION # 55
......

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