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The DAMA DQ-1220 Exam is designed to test the knowledge and skills of professionals who are responsible for managing data quality. DQ-1220 exam covers a range of topics, including data quality assessment, data profiling, data cleansing, and data governance. DQ-1220 exam is divided into multiple-choice questions, and candidates have 90 minutes to complete it.
The DAMA DQ-1220 exam is open to anyone who wants to demonstrate their expertise in data quality management. It is particularly beneficial for professionals working in data management, business intelligence, data warehousing, and analytics. Data Quality certification can help professionals improve their career prospects, increase their earning potential, and demonstrate their commitment to data quality.
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DAMA DQ-1220 (Data Quality) Exam is recognized globally as a benchmark for excellence in data quality management. It is a valuable certification that can enhance your career prospects and demonstrate your commitment to best practices in data quality management. Whether you are a seasoned data professional or just starting out in the field, DQ-1220 Exam is an excellent way to validate your skills and knowledge in data quality management.
NEW QUESTION # 15
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
Answer: D
Explanation:
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
NEW QUESTION # 16
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: C
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 # 17
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: D
Explanation:
An MDM investment is justified when the business value obtained from centralized coordination of shared data exceeds the additional cost of managing and integrating another controlled representation of that data.
This is the economic trade-off expressed by option C and is also the intended answer in published versions of this DAMA question.
DAMA-DMBOK2 positions Master Data Management as a mechanism for improving consistency and control over enterprise entities such as customers, products, suppliers, employees, and locations. Uncoordinated local copies create redundant maintenance, inconsistent definitions, duplicate records, reconciliation costs, and integration complexity. DAMA sources emphasize that controlling shared master and reference data reduces both cost and risk generated by inconsistencies between systems.
However, introducing an MDM repository also incurs costs: integration, stewardship, matching, survivorship logic, synchronization, governance, maintenance, and potentially another physical copy of shared information.
The business case must therefore demonstrate that improved coordination, quality, reuse, reporting, and operational consistency outweigh those costs.
MDM does not require all contributing systems to be decommissioned, nor does it eliminate Reference Data Management. Similarly, "mining golden records" is not the fundamental economic basis for MDM.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Business Drivers; Shared Data; Central Coordination; Data Integration; Data Quality and Stewardship.
NEW QUESTION # 18
Discovering and documenting metadata about physical data assets provides:
Answer: C
Explanation:
Discovering and documenting metadata about physical data assets provides visibility into how data moves and is transformed between systems. This is a core function of technical metadata and data lineage.
DAMA-DMBOK2 states that technical metadata describes the technical characteristics of data, the systems that store it, and the processes that move it within and between systems. Examples include physical table and column names, ETL job information, source-to-target mappings, and lineage documentation with upstream and downstream impact information. The certification question therefore points directly to option D; the same interpretation is independently reflected in published CDMP-oriented material.
This capability is critical to Data Quality because a defect observed in a report or downstream application may not originate in the current system. It may have been introduced through extraction logic, transformation rules, reference-data conversion, aggregation, or loading.
Documented physical metadata allows analysts to trace the affected value backward, identify its source, inspect each transformation, and isolate the point at which the defect occurred. It also supports change-impact analysis: modifying a source field or mapping can reveal which downstream assets may be affected.
Metadata therefore provides the evidence required for systematic root-cause analysis rather than symptom- based correction.
Reference Topics: DAMA-DMBOK2 Metadata Management - Technical Metadata; Source-to-Target Mapping; Data Lineage; Data Integration and Interoperability; Chapter 13 - Root-Cause Analysis and Quality Monitoring.
NEW QUESTION # 19
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
Answer: C
Explanation:
The correct definition is the fundamental organisation of a system, and the principles governing its design and evolution. DAMA-DMBOK2 incorporates the ISO/IEC 42010 architectural concept when establishing the theoretical basis for Data Architecture.
The fuller ISO formulation describes architecture in terms of the fundamental organization of a system embodied in its components, their relationships to one another and to the environment, together with the principles governing design and evolution. DAMA uses this concept to distinguish true architecture from a collection of diagrams or implementation specifications.
Option E is therefore incomplete: architectural artifacts document architecture, but the artifacts themselves are not the architecture. Option C focuses too narrowly on implementation and maintenance, while options B and D describe potential objectives or constraints rather than the definition.
Applied to enterprise data, this means architecture establishes the high-level organization of data assets, information flows, platforms, relationships, and guiding principles by which the data environment evolves.
Data Quality benefits because architectural principles establish where authoritative information originates, how it moves, how duplication is controlled, and where governance and quality controls must operate.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Architecture Definition; ISO/IEC 42010; Enterprise Data Architecture; Architectural Principles; Chapter 13 - Quality by Design.
NEW QUESTION # 20
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