Pass the DAMA CDMP DMF-1220 Questions and answers with ValidTests

Quality

Stewardship

Authority

Ownership

Exclusivity

Inclusivity

Define, approve, communicate, and implement principles, policies, procedures, metrics, tools, and responsibilities for data management

Monitor and guide policy compliance, data usage, and management activities

Help the data modelers to use data elements that have gone through Data Governance processes

Enable an organization to manage its data as an asset

Ensure that the privacy and confidentiality needs of all stakeholders are enforced and audited

Because it will show that the Data Governance team is working hard

It will give the Business some metrics

It is important because the organization will have an opportunity for business people to be identified as data stewards

It is important because it will prove that the IT department is best qualified to define, develop, and manage the organization’s data assets

It will identify the readiness and level of maturity that an organization will need to effectively govern and sustain its data, process, and policies

FALSE

TRUE

Meshing

Munging

Mirroring

Matching

Mangling

Data Quality must be managed throughout the data lifecycle

Data Security must only be managed at the start of the data lifecycle

Metadata Quality is the most important part of the management process

Data Management efforts should focus on the most critical data last

Strategy

Policy

Data Management Projects

Compliance

Oversight

All of the above

Inappropriate use of photocopier toner

Cloud-based databases

Software ignoring privacy tags on the data

Ineffective implementation of data architecture

Stored procedures being called directly

An effective service level agreement with defined rewards and penalties

An assignment process to appropriate individuals and teams

A standard vocabulary for classifying data quality issues

A well defined system of escalation based on the impact, duration, or urgency ofan issue

An operational workflow that ensures effective resolution

elational Model

Dimensional Model

Transition State Model

Process Model

Machine Leaming Model

Time variant

Database

Subject-orientated

Historical

Replacement cost

Market value

Selling Data

Risk cost

Identified opportunities

All of the above

Managed outside of the repository and only the results of data quality assessments stored as rules metadata

Documented consistently, tied to business impact, backed by data analysis, and accessible to all data consumers

Organized with metrics so the data stewards can review the data and see what rules apply

Focused on rules that can be integrated into application services

Focused on managing relationships that have gone wrong in the past and may go wrong in the future

Yes, since Standards Bodies typically supply reference data, the enterprise can automatically update when a new code or value is received

No, updates should always be made directly via data entry or through a specific batch interface based on operator-entered information partly because of regulatory reporting and archiving

Yes, you do not have to worry about archived data with reference data so tables can be updated automatically

No, but an enterprise can use program logic to do updates as there is little potential for problems to occur with reference data

Yes, older transactions do not have to be removed because with the Cloud there is unlimited database storage

DQM Procedures

Data architecture

Data governance

DQ Policies and guidelines

Analyses from data profiling

Data quality Service Level Agreements