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DAMA DQ-1220 (Data Quality) certification exam is a popular certification among professionals who work with data. Data Quality certification exam is designed to test a candidate's knowledge and skills related to data quality, which is an essential aspect of data management. Data Quality certification is offered by the Data Management Association (DAMA), which is a global organization that provides education, certification, and networking opportunities to data management professionals.
DAMA DQ-1220: Data Quality exam is a certification program designed to validate an individual's expertise in data quality management. DQ-1220 Exam is offered by the Data Management Association International (DAMA) and is recognized globally as a standard for measuring data quality proficiency. DQ-1220 exam is designed to test the candidate's knowledge of data quality concepts, principles, and best practices.
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Achieving the DAMA DQ-1220 certification not only validates an individual's proficiency in data quality management but also demonstrates a commitment to enhancing their skills and knowledge in this critical area. Data Quality certification can open up new career opportunities for professionals and help them differentiate themselves from their peers. Whether you are looking to advance your career in data quality management or improve your organization's data quality, the DAMA DQ-1220 Exam is an excellent way to achieve your goals.
NEW QUESTION # 120
A form permits users to enter any text into a field that should contain an ISO country code. What is the strongest preventive Data Quality control?
Answer: C
Explanation:
The strongest preventive control is to restrict data entry to the approved country-code domain. Prevention at the point of creation is generally more effective and less costly than repeatedly correcting invalid values downstream.
A controlled drop-down list, validated API, or reference-domain lookup ensures that users can select only permitted codes. This directly supports Validity and improves Consistency across applications.
The rule should be linked to centrally governed Reference Data rather than hard-coded independently in each application. That allows changes to the permitted domain to be managed systematically.
Downstream cleansing remains useful for historical defects or external data, but it should not be the primary control when the organization controls the source-entry process.
DAMA's framework links Reference/Master Data Management to consistency of shared values and Chapter
13 to defining and monitoring Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Validity; Data Creation; Chapter
10 - Reference Data; Controlled Domains.
NEW QUESTION # 121
A goal of reference and master data management is for data to ensure shared data is:
Answer: B
Explanation:
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
NEW QUESTION # 122
The Family Name of a Person is recorded in a system. The column name is Pname. Pname is an example of:
Answer: A
Explanation:
Pname is Metadata because it is the name assigned to a database column and therefore describes the structure in which the actual Family Name values are stored. A value such as Smith or Patel would be data; Pname is structural information describing that data.
DAMA-DMBOK2 defines Metadata broadly as information about data and data-related processes. Technical metadata includes physical database characteristics such as table names, column names, datatypes, lengths, indexes, constraints, and related structural properties. The interpretation of Pname as metadata is also consistent with the published version of this exact certification question.
The fact that Pname may be an unclear abbreviation does not change its classification. It may represent a poor naming convention from a usability or governance perspective, but technically it remains metadata. A stronger physical name might be FamilyName, while the metadata repository could additionally map that field to the authoritative business glossary term.
This distinction matters to Data Quality because rules are frequently attached to metadata objects. For example, the Family Name attribute might have completeness, permitted-character, maximum-length, and standardization requirements.
Effective Metadata Management connects the technical column to its business meaning, system lineage, stewardship, and applicable Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 11 - Technical Metadata; Database Metadata; Business Glossary; Chapter 13 - Data Quality Rules and Metadata.
NEW QUESTION # 123
An effective Data Governance communication program should include the following:
Answer: C
Explanation:
An effective Data Governance communication program should employ multiple complementary communication mechanisms, making All answers correct. Governance changes how people define, create, use, approve, and resolve issues with data; consequently, sustained adoption requires more than publishing policies.
Regular newsletters keep stakeholders aware of progress, decisions, metrics, and upcoming activities. A Data Governance Portal provides a persistent location for policies, standards, stewardship information, glossaries, issue processes, and supporting materials. Custom training develops the capabilities required for individuals to understand their specific governance responsibilities. Informal networking events help establish relationships across business and technical groups, which is particularly important when resolving data ownership and definition conflicts.
DAMA-DMBOK2 treats communication and organizational change as core implementation considerations because governance depends on participation across functions rather than on a single technical team.
Published CDMP material for this item identifies the combined response-newsletters, portal, training, and networking-as the intended answer.
For Data Quality, communication ensures that quality definitions, issue-management procedures, stewardship responsibilities, thresholds, and remediation decisions are understood and consistently applied.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Communications; Organizational Change; Training; Data Governance Portal; Stewardship Engagement; Chapter 13 - Data Quality Culture.
NEW QUESTION # 124
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
Answer: C
Explanation:
An enterprise Master Data Repository serving multiple applications is a classic Hub-and-Spoke integration pattern. In this architecture, shared information is consolidated physically or virtually in a central hub, while participating applications interact with that hub rather than building a separate direct interface to every other application.
DAMA-DMBOK2 explicitly identifies Master Data Management hubs, Data Warehouses, Data Marts, and Operational Data Stores as familiar examples of data hubs. The hub-and-spoke model reduces the proliferation of point-to-point interfaces and can provide a consistent enterprise view of shared data.
This architecture is particularly appropriate for MDM because customer, product, supplier, location, or other master entities often need to be standardized, matched, governed, and redistributed across many systems. The central hub can apply survivorship rules, reference mappings, stewardship decisions, and quality controls before distributing trusted values.
Replication and Change Data Capture are techniques for moving or detecting changes in data, but neither describes the overall interaction topology. Publish-subscribe may operate alongside an MDM hub as a distribution mechanism, but the enterprise repository itself represents the hub within a hub-and-spoke architecture.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Hub-and-Spoke; Integration Interaction Models; Chapter
10 - Master Data Management; Enterprise Master Data Hub; Data Quality Consistency.
NEW QUESTION # 125
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