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DAMA DMF-1220 Exam Syllabus Topics:

Section	Weight	Objectives
Reference and Master Data	5%	- Definitions and purpose - Management processes
Data Governance	15%	- Compliance and ethics - Governance framework and policies - Data stewardship
Data Quality	12%	- Assessment and improvement - Data cleansing - Quality dimensions
Data Architecture	12%	- Architecture components and layers - Enterprise data architecture - Data modeling and design
Data Integration and Interoperability	10%	- Data exchange standards - Integration patterns - ETL and ELT processes
Metadata Management	8%	- Types of metadata - Metadata lifecycle - Metadata repositories
Data Security	10%	- Privacy and protection - Access control - Security principles
Data Storage and Operations	8%	- Data lifecycle management - Database operations - Storage technologies
Data Management Concepts	10%	- Data management roles and responsibilities - DAMA-DMBOK framework - Definitions and principles
Data Modeling and Design	10%	- Modeling standards and methods - Conceptual, logical, physical models

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DAMA Data Management Fundamentals Sample Questions (Q677-Q682):

NEW QUESTION # 677
Data profiling is a form of data analysis used to inspect data and assess quality.

A. TRUE
B. FALSE

Answer: A

NEW QUESTION # 678
Please select the correct types of data stewards:

A. Executive Data Steward
B. Business Data Steward
C. Enterprise Data Steward
D. A Data Seller
E. All of the above
F. Chief Data Steward

Answer: A,B,C,F

NEW QUESTION # 679
ISO 8000 will describe the structure and the organization of data quality management, including:

A. Data Quality Improvement
B. Data Quality Planning
C. Data Quality Assurance
D. Data Quality Availability
E. Data Quality Control

Answer: A,B,C,E

NEW QUESTION # 680
The goals of Data Integration and Interoperability include:

A. Identify meaningful events and automatically trigger alerts and actions.
B. Managing the availability of data throughout the data lifecycle
C. Provide the starting point for customizations, integration or even replacement of an application
D. Support business intelligence, analytics, master data management and operational efficiency efforts.
E. Provide data securely, with regulatory compliance, in the format and timeframe needed.
F. Lower cost and complexity of managing solutions by developing shared models and interfaces.

Answer: A,D,E,F

NEW QUESTION # 681
A data lake and a data warehouse are the same concepts in so far as:

A. They are both concerned with preparing data for reporting and analytics
B. They are both concerned with duplicating all the operational data
C. They are both concerned with using different forms of data governance
D. They are not related in any way
E. They are both concerned with producing star schemas and dimensional data models

Answer: A

Explanation:
A data lake and a data warehouse differ significantly in architecture, structure, processing, and governance, but both ultimately support the broader objective of making enterprise data available for reporting, analytics, and decision-making . A data warehouse typically contains integrated, cleansed, structured, and purpose- oriented data optimized for BI workloads. A data lake generally stores larger volumes of structured, semi- structured, and unstructured data, often retaining information closer to its original form for later processing and analysis. They therefore do not necessarily duplicate all operational data, and data lakes are not inherently based on dimensional or star-schema models. Both also require appropriate governance rather than fundamentally different forms of governance. Option B captures their principal common business purpose.
DAMA recognizes Data Warehousing/BI and modern data platforms as components of the broader analytical data-management landscape.

NEW QUESTION # 682
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