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As the world becomes more and more data-driven, ensuring the quality of the data is becoming increasingly important. The Data Management Association International (DAMA) has developed a certification program, known as the DAMA DQ-1220 Exam, to test individuals' knowledge and skills in the area of data quality.
DAMA DQ-1220 Certification Exam is suitable for individuals who are involved in data management, such as data analysts, data architects, data quality managers, and data stewards. It is also beneficial for professionals who work in related fields, such as business intelligence, data warehousing, and software development. Data Quality certification is an excellent way for individuals to enhance their career prospects and increase their earning potential.
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Data quality is one of the most critical aspects of data management, and it is crucial for organizations to ensure that their data is accurate, complete, and consistent. To achieve this, many organizations are now seeking professionals who are skilled in data quality management. This has led to the rise of the DAMA DQ-1220 (Data Quality) Certification Exam, which is designed to test the knowledge and skills of individuals in the field of data quality.
NEW QUESTION # 79
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 # 80
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
Answer: C
Explanation:
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
NEW QUESTION # 81
A report value is incorrect, and the analyst needs to identify every source field and transformation used to calculate it. Which capability is most important?
Answer: D
Explanation:
The required capability is Data Lineage. Lineage documents how data originates, moves, transforms, and ultimately appears in downstream reports, applications, and analytical products.
When a report value is wrong, lineage allows investigators to trace the value backwards through calculations, mappings, staging structures, integration processes, source tables, and potentially external providers. This prevents teams from guessing where the defect originated.
Metadata Management is the principal discipline responsible for collecting and maintaining this contextual information. DAMA's public framework identifies lineage and definitions as central Metadata Management capabilities.
Lineage is also essential for change-impact analysis. If a source element changes, teams can determine which reports, models, or applications depend upon it.
For Data Quality, lineage supports root-cause analysis and coordinated remediation. A downstream value may be accurate relative to the transformation applied but still wrong because the source or transformation logic was incorrect.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Source-to-Target Mapping; Root-Cause Analysis; Data Integration.
NEW QUESTION # 82
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: B
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 83
A Data Quality team finds that 78% of customer complaints are caused by four defect categories out of twenty recorded categories. Which tool is most appropriate for prioritizing improvement activity?
Answer: B
Explanation:
Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.
The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.
The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship.
Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.
Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.
Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.
NEW QUESTION # 84
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