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DAMA DQ-1220 Exam is a comprehensive certification that tests your knowledge and skills in managing data quality. It covers various topics related to data quality management, including data quality concepts, data quality assessment, data profiling, data cleansing, and data quality metrics. Passing the exam is a significant achievement that demonstrates your expertise in data quality management and can help you advance your career in data management.
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DAMA DQ-1220 Certification Exam covers a wide range of topics related to data quality management, including data profiling, data quality assessment, data cleansing, data governance, and data stewardship. DQ-1220 exam is available in both online and in-person formats and is open to anyone interested in earning a credential in data quality management. Successful completion of the exam demonstrates an individual's ability to effectively manage data quality in a variety of organizational contexts, making them a valuable asset to any organization seeking to improve their data quality practices.
NEW QUESTION # 13
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 # 14
A customer's date of birth is stored as 12/04/1980. The value conforms to the database datatype and accepted date format, but the customer's verified date of birth is 21/04/1980. Which Data Quality dimension is primarily violated?
Answer: D
Explanation:
The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real- world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.
This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible-for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.
The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.
The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.
NEW QUESTION # 15
The DMBoK 'Environmental Factors hexagon' shows the relationship between:
Answer: C
Explanation:
The DAMA-DMBOK2 Environmental Factors Hexagon illustrates the relationship between People, Process, and Technology. It provides a visual key for interpreting the Knowledge Area Context Diagrams and emphasizes that effective Data Management depends on the coordinated interaction of these three dimensions.
DMBOK2 places Goals and Principles at the center of the hexagon because they guide how people perform activities and how technology is selected and used. People-related factors include roles, responsibilities, organization, and culture. Process-related factors include activities, techniques, and methods. Technology- related factors include tools and the mechanisms that support Data Management deliverables.
This model is especially relevant to Data Quality. A profiling or cleansing platform alone cannot establish sustainable quality. The organization also needs accountable Data Owners and Data Stewards, repeatable issue-management and improvement processes, business-defined rules, and governance mechanisms.
Conversely, well-designed processes will fail if supporting technologies cannot monitor or enforce required controls.
Option A relates more closely to the structure of DMBOK Knowledge Area Context Diagrams. The DAMA Wheel represents the Knowledge Areas, while the Environmental Factors Hexagon specifically emphasizes the People-Process-Technology relationship.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; Environmental Factors Hexagon; People, Process and Technology; Chapter 13 - Data Quality Organization, Processes and Tools.
NEW QUESTION # 16
Data governance represents:
Answer: C
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 # 17
A form permits users to enter any text into a field that should contain an ISO country code. What is the strongest preventive Data Quality control?
Answer: B
Explanation:
The strongest preventive control is to restrict data entry to the approved country-code domain. Prevention at the point of creation is generally more effective and less costly than repeatedly correcting invalid values downstream.
A controlled drop-down list, validated API, or reference-domain lookup ensures that users can select only permitted codes. This directly supports Validity and improves Consistency across applications.
The rule should be linked to centrally governed Reference Data rather than hard-coded independently in each application. That allows changes to the permitted domain to be managed systematically.
Downstream cleansing remains useful for historical defects or external data, but it should not be the primary control when the organization controls the source-entry process.
DAMA's framework links Reference/Master Data Management to consistency of shared values and Chapter
13 to defining and monitoring Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Validity; Data Creation; Chapter
10 - Reference Data; Controlled Domains.
NEW QUESTION # 18
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