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DAMA DQ-1220 Certification Exam is recognized globally as a standard for measuring the competency of data quality professionals. It is a comprehensive exam that assesses the candidate's understanding of the principles, techniques, and methodologies used in data quality management. DQ-1220 exam is designed to evaluate the candidate's ability to identify and resolve data quality issues, develop and implement data quality plans, and measure the effectiveness of data quality initiatives. Passing the DAMA DQ-1220 Certification Exam demonstrates that the candidate has the knowledge and skills necessary to ensure that organizational data is accurate, reliable, and trustworthy.
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The DAMA DQ-1220 exam covers various topics related to data quality, such as data profiling, data cleansing, data governance, and data integration. It also emphasizes the importance of data quality in business decision-making and how it can impact an organization's overall success. DQ-1220 exam is designed for professionals in the data management field, including data analysts, data scientists, data architects, and data quality professionals.
DAMA DQ-1220 Exam is a rigorous certification program that requires candidates to demonstrate a high level of proficiency in data quality management. DQ-1220 exam consists of 100 multiple-choice questions and has a duration of 2 hours. To pass the exam, candidates must score a minimum of 70%. DQ-1220 exam is offered online and can be taken from anywhere in the world.
NEW QUESTION # 86
An organization changes the definition of "Net Revenue". Before implementing the change, it wants to identify every report, transformation, and analytical model that depends on the existing definition. Which capability is most important?
Answer: C
Explanation:
The required capability is Metadata lineage and impact analysis. Changing a business definition can affect far more than the glossary entry itself. The organization needs to know which physical fields, transformation rules, ETL jobs, calculations, reports, dashboards, analytical models, and downstream datasets depend on the current definition.
Lineage shows where data originates and how it flows and transforms. Impact analysis follows those relationships forward to determine what will be affected by a proposed change.
This prevents a common Data Quality failure in which a definition changes in one area while downstream systems continue applying the old interpretation. The result can be technically correct calculations that are semantically inconsistent.
A governed change should therefore update the business glossary, transformation metadata, Data Quality rules, reporting definitions, and affected documentation in a coordinated manner. Appropriate Data Stewards and Data Owners should approve the new meaning and effective date.
DAMA's framework describes Metadata Management as enabling understanding through definitions, lineage, and usage across systems, while the revised Chapter 13 explicitly strengthens the Data Quality-Metadata relationship.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Impact Analysis; Business Glossary; Data Quality Consistency.
NEW QUESTION # 87
Data governance represents:
Answer: A
Explanation:
DAMA-DMBOK2 explicitly states that Data Governance represents an inherent separation of duty between oversight and execution. It illustrates the principle through an analogy with financial governance: an auditor exercises oversight over financial processes without personally executing financial management. Similarly, Data Governance ensures that data is properly managed but does not itself perform every operational Data Management activity.
This separation is fundamental because governance must retain sufficient independence to establish rules, monitor compliance, resolve conflicts, assign accountability, and evaluate whether operational teams are managing data according to approved policies and standards.
Option B describes activities in which governance and Data Quality practitioners may collaborate, but it is not the defining governance principle. Option D is also incorrect because federated governance is only one possible operating model; DMBOK2 also recognizes centralized and replicated approaches. Option E describes an organizational implementation, not the conceptual distinction.
Within Data Quality Management, this means that governance may approve quality policies, Critical Data Elements, acceptable thresholds, stewardship structures, and escalation procedures, while DQ analysts and operational teams perform profiling, cleansing, monitoring, and remediation.
Maintaining this division reduces conflicts of interest and establishes accountability for whether data- management activities achieve agreed business objectives.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Essential Concepts; Oversight versus Execution; Governance Operating Models; Chapter 13 - Data Quality Governance and Operational Responsibilities.
NEW QUESTION # 88
An effective Data Governance communication program should include the following:
Answer: B
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 # 89
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: B
NEW QUESTION # 90
Data Staging areas are populated from source databases using extract functions, transformed and then:
Answer: A
Explanation:
The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse.
DAMA-oriented training material presents this exact progression.
The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.
From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.
Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 - Profiling, Cleansing, Standardization and Validation.
NEW QUESTION # 91
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