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DAMA DMF-1220 Exam Syllabus Topics:

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

Section

Objectives

Topic 1: Data Management Fundamentals

- Content and Document Management

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

- Data Modeling and Design

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

- Data Quality

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

- Data Security

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

- Data Strategy

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

- Data Storage and Operations

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

- Data Ethics

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

- Data Governance

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

- Data Risk Management

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

- Data Integration and Interoperability

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

- Metadata Management

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

- Data Business and Data Management

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

- Data Architecture

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

- Master Data and Reference Data

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

- Data Warehousing and Business Intelligence

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

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DAMA Data Management Fundamentals Sample Questions (Q202-Q207):

NEW QUESTION # 202
The categories of the Data Model Scorecard with the highest weightings include:

A. How structurally sound is the model?
B. How well does the model capture the requirements?
C. None of the above
D. All of the above
E. How complete is the model?
F. How good are the definitions?

Answer: A,B,E

NEW QUESTION # 203
Inputs in the reference and master data context diagram include:

A. Cultural Drivers
B. Business Drivers
C. None of the above
D. All of the above
E. Data Glossary
F. Business model

Answer: B,E

NEW QUESTION # 204
Which of the following provides the strongest tangible reason for driving initiation of a Data Governance process in an enterprise?

A. I've been to a conference and found that lots of our competitors are doing it
B. There is typically significant waste in an enterprise through duplication of reports and metrics
C. Each division of an enterprise may have its own way to describe its business and associated metrics
D. 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)
E. There is disagreement among divisions on the meaning of key metrics

Answer: B

NEW QUESTION # 205
A report displaying birth date contains possible, but incorrect values. What is a possible explanation?

A. Birth date is populated from two source systems, one of which stores marriage date in the birth date field
B. Birth date is populated from a single source system, which contains missing values
C. Birth date is populated from a single source system, which does not contain birth date
D. Birth date is populated from two source systems, both of which record the birth date in the birth date field
E. Birth date is populated from a single source system, where the date field is an offset value of 1601

Answer: A

Explanation:
The correct answer is A. The scenario describes values that are syntactically possible as dates but semantically wrong for the Birth Date attribute. DAMA data quality practice distinguishes validity from accuracy: a value may satisfy the required format or domain yet still fail to represent the real-world fact correctly. If one source system places a marriage date in the birth-date field, the resulting value can look like a perfectly valid date while being factually incorrect. Missing values would primarily indicate a completeness problem, while a system that does not contain birth date would not normally supply a plausible but incorrect birth date without some additional mapping defect. When both sources correctly record birth date, there is no inherent reason for inaccuracy. Therefore, the source-to-target semantic mismatch in option A best explains the report.

NEW QUESTION # 206
All organizations have the same Master Data Management Drivers and obstacles.

A. TRUE
B. FALSE

Answer: B

NEW QUESTION # 207
......

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