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DAMA DQ-1220 Certification Exam is a rigorous exam that requires a deep understanding of data quality concepts and practices. DQ-1220 exam consists of 100 multiple-choice questions, and candidates have three hours to complete it. DQ-1220 Exam is only available in English and can be taken at any Pearson VUE testing center worldwide.
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The benefits of obtaining the DAMA DQ-1220 Certification are numerous. For professionals, it provides them with a competitive edge in the job market and validates their skills and knowledge in data quality management. For organizations, it ensures that their data management professionals have the necessary expertise to maintain high-quality data and improve their overall data-driven decision-making. Additionally, the certification program promotes a standardized approach to data quality management and helps establish industry best practices.
NEW QUESTION # 20
A company requires every system to use the same ISO country-code list managed centrally. This is primarily an example of:
Answer: A
Explanation:
This is Reference Data Management. Country codes represent a controlled set of values used to classify or contextualize other data. Managing them centrally helps ensure that all systems interpret the same codes consistently.
Without centralized governance, one system might use GB, another UK, and another proprietary numeric values. Those differences create mapping complexity, failed integrations, inaccurate aggregation, and inconsistent reporting.
Reference Data Management establishes authoritative sources, stewardship, allowed values, mappings, effective dates, distribution processes, and change control. DAMA's public framework describes Reference and Master Data Management as ensuring consistency in core shared information across organizational units.
The Data Quality dimensions most directly supported are Validity and Consistency. Validity ensures values belong to an approved domain; consistency ensures equivalent concepts are represented compatibly across systems.
Metadata should document the standard, meaning, owner, and version of each reference domain.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference Data; Controlled Code Sets; Authoritative Sources; Chapter 13 - Validity and Consistency.
NEW QUESTION # 21
Critical to the incremental development of the data warehouse is:
Answer: A
Explanation:
A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA- DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.
A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.
This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures.
Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.
Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.
Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.
Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence - Maintain Data Products; Release Management; Incremental Development; Chapter 13 - Quality Monitoring and Change Control.
NEW QUESTION # 22
When a data quality team has more issues than they can manage, they should look to:
Answer: D
Explanation:
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
NEW QUESTION # 23
Critical Data is most often used in:
Answer: C
Explanation:
All of these is correct because DAMA-DMBOK2 defines criticality according to the business use and impact of the data rather than by a particular technical characteristic. Critical Data Elements are commonly associated with regulatory, financial, or management reporting; operational business needs; measurement of product quality and customer satisfaction; and strategic initiatives such as competitive differentiation. DAMA's revised Chapter 13 also explicitly adds and clarifies the concept of Critical Data Elements within the Data Quality framework.
The purpose of identifying critical data is prioritization. Organizations cannot apply identical levels of profiling, monitoring, stewardship, remediation, and control to every data element. Data whose failure could create substantial operational, financial, legal, regulatory, customer, or reputational impact receives greater management attention.
For example, values feeding regulatory reports require stringent accuracy and traceability; operational data may require high availability and timeliness; customer-satisfaction measures require reliable and consistent inputs; and strategically important data can affect competitive decisions. Current DAMA-aligned material confirms these same usage categories for Critical Data Elements.
Once Critical Data Elements are identified, they should be linked to business definitions, Data Owners and Stewards, quality dimensions, measurable rules, lineage, authoritative sources, thresholds, and issue- management processes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Business Drivers; Data Quality Requirements; Prioritization; Data Governance; Metadata Management.
NEW QUESTION # 24
Descriptive metadata describes a resource, enabling its identification and retrieval.
Answer: B
Explanation:
DAMA-DMBOK2 distinguishes descriptive, structural, and administrative metadata when discussing metadata classifications used outside conventional IT contexts. Descriptive metadata identifies and describes a resource sufficiently to enable discovery and retrieval. DMBOK2 gives title, author, and subject as explicit examples of descriptive metadata.
A book's author, title, and subject therefore answer practical discovery questions: What is the resource? Who created it? What is it about? These attributes allow catalogues, repositories, search tools, and users to identify and retrieve the required resource.
Option A is principally structural metadata. DMBOK2 specifically identifies the number of pages and chapters as examples describing relationships within or among the components of a resource. Option D represents administrative metadata because version numbers and archive dates help manage the resource throughout its lifecycle.
The distinction is important to Data Quality because metadata itself must have appropriate completeness, consistency, currency, and accuracy. Incorrect descriptive metadata can make a valid data asset effectively undiscoverable or lead users to select an inappropriate source.
Metadata Management therefore supports Data Quality by giving data consumers reliable definitions, context, provenance, ownership, lineage, and usage information.
Reference Topics: DAMA-DMBOK2 Metadata Management - Descriptive Metadata; Structural Metadata; Administrative Metadata; Metadata Quality; Chapter 13 - Data Quality and Metadata Management.
NEW QUESTION # 25
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