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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Topic 2: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Topic 3: Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Topic 4: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Topic 5: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
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NEW QUESTION # 39
A Data Quality rule has been implemented and the defect rate has fallen below the agreed threshold. What activity should continue?
Answer: A
Explanation:
The organization should continue monitoring the quality metric. A successful remediation does not guarantee that the underlying process will remain controlled indefinitely.
Systems change, source applications are upgraded, personnel and suppliers change, reference values evolve, interfaces are modified, and business rules are revised. Any of these events can cause previously corrected defects to reappear.
A mature Data Quality lifecycle therefore treats improvement as continuous rather than as a one-time cleansing exercise. DAMA's revision of Chapter 13 specifically clarifies the Data Quality Improvement Lifecycle and links it to established continuous-improvement cycles.
Monitoring should confirm that the result remains within agreed thresholds and should provide trend information so deterioration can be detected before it creates significant business impact.
The frequency of monitoring may be reduced after a process demonstrates sustained stability, but the decision should be risk-based. Critical Data Elements generally justify stronger ongoing oversight.
The rule, threshold, owner, and measurement method should remain documented as metadata.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Control; Continuous Improvement; Metrics; Thresholds; Data Quality Scorecards.
NEW QUESTION # 40
Examples of transformation include:
Answer: E
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 # 41
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
Answer: D
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 # 42
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Answer: D
Explanation:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
NEW QUESTION # 43
Data Staging areas are populated from source databases using extract functions, transformed and then:
Answer: A
Explanation:
The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse.
DAMA-oriented training material presents this exact progression.
The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.
From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.
Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 - Profiling, Cleansing, Standardization and Validation.
NEW QUESTION # 44
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