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DAMA DQ-1220 exam involves questions that cover various topics, including the fundamental concepts of data quality, data quality assessment, and measurement, data profiling, the impact of business rules on data quality, data quality issue identification and remediation, data quality improvement, data quality strategy, data quality frameworks, data quality standards, data governance and stewardship, and many more.
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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Achieving the DQ-1220 Certification demonstrates a high level of competence in data quality management and can enhance the career prospects of professionals in this field. It also provides organizations with a benchmark for assessing the skills and knowledge of their employees in data quality management.
NEW QUESTION # 72
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
Answer: D
Explanation:
The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.
Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.
The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.
This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.
The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.
Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.
Reference Topics: DAMA-DMBOK2 - Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.
NEW QUESTION # 73
A report displaying birth date contains possible, but incorrect values. What is a possible explanation?
Answer: E
Explanation:
The critical wording is "possible, but incorrect values." This describes values that satisfy basic syntactic or domain validation-they look like legitimate dates-but do not accurately represent the real-world attribute defined by the field.
If two systems contribute data and one maps marriage date into the birth-date field, the resulting values can be perfectly valid calendar dates while being semantically incorrect as birth dates. This is principally an Accuracy defect, because DAMA defines accuracy in terms of how correctly data represents the real-world object or event it is intended to describe. It may also expose a consistency and integration-mapping problem between source systems. DAMA's quality framework distinguishes accuracy from completeness: data can be populated and formally valid while still being factually wrong.
Missing values would primarily produce a Completeness defect rather than populated-but-incorrect values.
Two correctly mapped systems would not inherently explain the problem. An offset or technical date representation could create transformation problems, but the scenario most directly illustrates semantic mis- mapping between data elements.
The appropriate remediation is therefore not simple cleansing alone. Metadata mappings, source-to-target specifications, lineage, business definitions, and integration rules should be corrected at the root cause.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy, Completeness and Consistency; Root-Cause Remediation; Data Profiling; Metadata Management; Data Integration and Interoperability.
NEW QUESTION # 74
A goal of reference and master data management is for data to ensure shared data is:
Answer: A
Explanation:
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
NEW QUESTION # 75
A Data Quality team repeatedly corrects invalid postal codes in the warehouse, but the same errors reappear after every nightly load. What is the most appropriate long-term response?
Answer: B
Explanation:
The correct long-term response is to remove the root cause in the source or integration process. Repeated warehouse cleansing treats symptoms. If the defect is recreated every night, operational costs continue and downstream systems remain exposed until the cleansing process executes.
DAMA Data Quality practice emphasizes sustained improvement rather than repeated correction. The quality lifecycle therefore includes identifying defects, determining business impact, analyzing root causes, implementing corrective actions, and monitoring results. The current Chapter 13 revision specifically strengthens clarification of the Data Quality Improvement Lifecycle and responsibilities within it.
The underlying cause might be weak source validation, an incorrect source-to-target mapping, outdated Reference Data, transformation logic, or missing governance over postal-code rules.
Cleansing remains appropriate where historical data must be repaired or immediate downstream protection is required. However, preventive control should be introduced as close to the creation point as practical.
Metadata and lineage help trace the postal code through the data flow, while governance establishes who has authority to change the offending process.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Remediation; Prevention; Cleansing; Data Integration and Lineage.
NEW QUESTION # 76
Critical to the incremental development of the data warehouse is:
Answer: B
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 # 77
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