DMF-1220 Practice Questions

A.  

Promote consistent results but periodically reviewed and updated

B.  

Be a guideline for the organization but open to interpretation

C.  

Promote consistent results so only written once and never updated

D.  

Only be necessary for the Data Governance team

E.  

Set by an standards organization and not by the enterprise

A.  

Structural Metadata

B.  

Descriptive Metadata

C.  

Generic Metadata

D.  

Administrative metadata

E.  

Conceptual Metadata

A.  

Impacts of regulations, the value of data to the organization, data modelling skills, & third-party data policies

B.  

Data inputs, context diagrams, methods and procedures

C.  

Data management skills, budget considerations & organization strategy

D.  

Cultural factors, the role of a data steward, & third-party data policies

E.  

A model that documents the business, cultural factors, the impacts of regulations, and the value of data to the organization

A.  

Cultural factors. Governance strategy will need to advocate for an operating model that fits with organizational culture, while still progressing change

B.  

What is their Business Model (decentralized vs. centralized, local vs. international, etc.)

C.  

Impact of regulation. Highly regulated organizations will have a different mindset and operating model of Data Governance than those less regulated

D.  

All of these

E.  

Value of the data to the organization. Is their Data a crucial commodity (do they sell the data)

A.  

Align data architecture with enterprise strategy and business architecture

B.  

Provide a standard business vocabulary for data and components

C.  

Outline high-level integrated designs to meet these requirements.

D.  

Integrate with overall enterprise architecture roadmap

E.  

Define the current state of data in the organization.

F.  

Express strategic data requirements

A.  

Extract Transpose Leverage

B.  

Export Transform Log

C.  

Extend Trim Load

D.  

Extract Transform Load

E.  

Efficient Trace Logging

A.  

Data is an asset with unique properties.

B.  

It takes Metadata to manage data.

C.  

The most important part of data management is security.

D.  

Data management is lifecycle management.

E.  

Effective data management requires leadership commitment.

F.  

Efficient data management requires a team of IT professionals only.

A.  

Standard measurement methodologies

B.  

Data security standards

C.  

Data remediation standards and procedures

D.  

None of the above

E.  

Data quality rules

F.  

Data quality rules

A.  

Business Architecture

B.  

Data Architecture

C.  

Data Management Architecture

D.  

Systems Architecture

E.  

Application Architecture

F.  

Technology Architecture

A.  

Strategic Alignment Model

B.  

DAMA DMBOK Hexagon

C.  

DAMA Wheel

D.  

Amsterdam Information Model

A.  

Choosing a maturity model for an organization is expensive

B.  

Vendors may go out of business so organizations need to choose wisely

C.  

Performing an assessment without evaluating multiple models does not provide the organization with the ability to use the easiest model

D.  

Each maturity model is flawed and the organization must find the flaws and work around them

E.  

Each DMMM is based on a specific approach and needs to align with an organization's needs

A.  

Definitional conformance

B.  

Format compliance

C.  

Range conformance

D.  

Mapping conformance

A.  

The roadmap shows how to implement the important assessment results, and when to do so

B.  

A roadmap will highlight items in an implementation sequence and on other considerations (budget, resources, other programs, etc.)

C.  

A roadmap is just another way of saying you should have a plan

D.  

A roadmap is the only way for the organization to understand the results of the assessment

E.  

A roadmap includes a project plan, which gives the stakeholders information on how each recommendation will be implemented

A.  

Column Properties

B.  

Access permissions

C.  

Internal

D.  

Conceptual

E.  

ETL job details

A.  

Staging areas

B.  

Data marts

C.  

Cubes

D.  

Kubernetes