Page |1
DAMA DMBOK Questions and Correct
Answers/ Latest Update / Already Graded
Data governance programs must be:
Ans: 1. Sustainable 2. Embedded 3. Measured
Datagovernance vs datamanagement
Ans: Data governance = Ensuring data is managed (oversight)
Data management= Managing data to achieve goals (execution)
Data Governance Steering Committee
Ans: Primary and highest authority organization for data
governance, responsible for oversight, support and funding
data governance activities
Data Governance Council
Ans: Manages data governance initiatives, issues and
escalations
Data Governance Office
All rights reserved © 2025/ 2026 |
, Page |2
Ans: Ongoing focus on enterprise-level data definitions and
data management standards (data stewards, data owners)
Centralized model (Data Governance)
Ans: One data governance organization oversees all activitites
in all subject areas
Replicated model (Data Governance)
Ans: the same DG operating model and standards are adopted
by each business unit
Federated model
Ans: One DG organization coordinates with multiple business
units
Types of data stewards
Ans: Chief data stewards, executive data stewards, enterprise
data stewards, business data stewards, data owner, technical
data stewards, coordinating data stewards
Ways to measure value of data
All rights reserved © 2025/ 2026 |
, Page |3
Ans: Replacement costs, market value, identified
opportunities, selling data and risk cost
Deliverables of a DG strategy
Ans: Charter, operating framework and accountabilitites,
Implementation roadmap, plan for operational succes
Issue management is the process for identifying, quantifying,
prioritizing and resolving data governance related issues, including:
Ans: Authority, change management escalations, compliance,
conflicts, conformance, contracts, data security and identity,
data quality issues.
A business glossary is a core DG tool. It houses agreed-upon definitions
of business terms and relates these to data. Objectives of a business
glossary:
Ans: enable common understanding, reduce risks that data will
be misused, improve alignment between technology assets and
the business organization, maximize search capability
Primary data architecture outcomes include:
All rights reserved © 2025/ 2026 |
, Page |4
Ans: Data storage and processing requirements & designs of
structures and plans that meet the current and long -term data
requirements of the enterprise
Deliverables of data architecture:
Ans: Data architecture design, data flows, data value chains,
enterprise data model, implementation roadmap
Enterprise Data Model (EDM)
Ans: holistic, enterprise level conceptual or lofical data model
providing a common consistent view of data across the
enterprise. A high-level, simplified data model. Includes key
enterprise entitites, relationships, business rules and attributes
Data flow design
Ans: Defines the requirements and master blueprint for
storage and processing across databases, applications,
platforms, and networks. Data flows map the movement of data
to business processes, locations, business roles and technical
components
Data flows map and document relationships between data and (....):
All rights reserved © 2025/ 2026 |
DAMA DMBOK Questions and Correct
Answers/ Latest Update / Already Graded
Data governance programs must be:
Ans: 1. Sustainable 2. Embedded 3. Measured
Datagovernance vs datamanagement
Ans: Data governance = Ensuring data is managed (oversight)
Data management= Managing data to achieve goals (execution)
Data Governance Steering Committee
Ans: Primary and highest authority organization for data
governance, responsible for oversight, support and funding
data governance activities
Data Governance Council
Ans: Manages data governance initiatives, issues and
escalations
Data Governance Office
All rights reserved © 2025/ 2026 |
, Page |2
Ans: Ongoing focus on enterprise-level data definitions and
data management standards (data stewards, data owners)
Centralized model (Data Governance)
Ans: One data governance organization oversees all activitites
in all subject areas
Replicated model (Data Governance)
Ans: the same DG operating model and standards are adopted
by each business unit
Federated model
Ans: One DG organization coordinates with multiple business
units
Types of data stewards
Ans: Chief data stewards, executive data stewards, enterprise
data stewards, business data stewards, data owner, technical
data stewards, coordinating data stewards
Ways to measure value of data
All rights reserved © 2025/ 2026 |
, Page |3
Ans: Replacement costs, market value, identified
opportunities, selling data and risk cost
Deliverables of a DG strategy
Ans: Charter, operating framework and accountabilitites,
Implementation roadmap, plan for operational succes
Issue management is the process for identifying, quantifying,
prioritizing and resolving data governance related issues, including:
Ans: Authority, change management escalations, compliance,
conflicts, conformance, contracts, data security and identity,
data quality issues.
A business glossary is a core DG tool. It houses agreed-upon definitions
of business terms and relates these to data. Objectives of a business
glossary:
Ans: enable common understanding, reduce risks that data will
be misused, improve alignment between technology assets and
the business organization, maximize search capability
Primary data architecture outcomes include:
All rights reserved © 2025/ 2026 |
, Page |4
Ans: Data storage and processing requirements & designs of
structures and plans that meet the current and long -term data
requirements of the enterprise
Deliverables of data architecture:
Ans: Data architecture design, data flows, data value chains,
enterprise data model, implementation roadmap
Enterprise Data Model (EDM)
Ans: holistic, enterprise level conceptual or lofical data model
providing a common consistent view of data across the
enterprise. A high-level, simplified data model. Includes key
enterprise entitites, relationships, business rules and attributes
Data flow design
Ans: Defines the requirements and master blueprint for
storage and processing across databases, applications,
platforms, and networks. Data flows map the movement of data
to business processes, locations, business roles and technical
components
Data flows map and document relationships between data and (....):
All rights reserved © 2025/ 2026 |
