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DAMA DQ-1220 (Data Quality) Exam is a certification exam that focuses on the importance of data quality in today's organizations. Data quality is essential for organizations to make accurate decisions and achieve their business objectives. The DAMA DQ-1220 Exam is designed to help professionals develop their understanding of data quality and become proficient in implementing data quality programs in their organizations.
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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.
NEW QUESTION # 84
Examples of transformation include:
Answer: A
Explanation:
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
NEW QUESTION # 85
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: C
Explanation:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
NEW QUESTION # 86
The role of Metadata in Data Management is:
Answer: E
Explanation:
The broad role of Metadata is to help organizations understand their data, systems, and workflows. Metadata provides the context required to interpret data correctly and connect business meaning with technical implementation and operational processing. This answer is also explicitly identified in DAMA-oriented certification material.
Business metadata explains concepts, definitions, rules, ownership, stewardship, and terminology. Technical metadata describes tables, columns, datatypes, interfaces, transformations, and system structures. Operational metadata captures information about processing, execution, schedules, and usage. Together, these categories explain what information means, where it resides, how it moves, and how it is used.
Metadata can certainly improve decision-making, support Big Data solutions, group concepts, and assist reporting, but those are secondary outcomes. Option C expresses its enterprise-wide role most comprehensively.
Its connection to Data Quality is fundamental. Quality cannot be assessed consistently without metadata explaining the intended meaning and acceptable characteristics of a data element. Lineage allows a defect to be traced to its origin, while business definitions establish the context for accuracy and validity measurements.
Reference Topics: DAMA-DMBOK2 Metadata Management - Business, Technical and Operational Metadata; Data Lineage; Business Glossary; Chapter 13 - Data Quality Rules and Metadata Dependencies.
NEW QUESTION # 87
A customer is shown as "Active" in the CRM but "Closed" in the billing system at the same point in time.
Both systems use the same customer identifier. Which Data Quality dimension is primarily affected?
Answer: C
Explanation:
The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.
The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation.
However, unless a legitimate business rule explains the difference, the two representations are inconsistent.
Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of
"Active" and "Closed," examine synchronization timing, and trace transformation or integration logic.
Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.
Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.
DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.
NEW QUESTION # 88
A team uses a fishbone diagram during investigation of recurring missing customer email addresses. What is the primary purpose of the diagram?
Answer: D
Explanation:
A fishbone diagram, also known as an Ishikawa or cause-and-effect diagram, is used to organize and explore potential root causes of a problem.
For recurring missing email addresses, categories might include People, Process, Technology, Data, Policy, Training, and External Sources. Potential causes could include optional form design, unclear business requirements, API mapping defects, missing supplier data, or inconsistent onboarding procedures.
The technique helps prevent teams from prematurely assuming that the most visible symptom is the actual cause. It encourages structured investigation across multiple dimensions of the process.
Once hypotheses are identified, evidence should be collected to confirm or reject them. The diagram itself does not prove causation.
In Data Quality programs, root-cause analysis is essential because repeated cleansing without process correction allows defects to recur. DAMA's Chapter 13 revision explicitly retains and strengthens the treatment of common causes and the improvement lifecycle.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Cause-and-Effect Diagram; Issue Management; Continuous Improvement.
NEW QUESTION # 89
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