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Die DAMA DQ-1220 (Data Quality) Prüfung ist ein Zertifizierungsprogramm, das entwickelt wurde, um die Kompetenz eines Kandidaten im Bereich des Datenqualitätsmanagements zu testen. Die Prüfung deckt eine Vielzahl von Themen im Zusammenhang mit der Datenqualität ab, wie z.B. Datenprofilierung, Datenbereinigung, Datenstandardisierung, Datenauditierung und Datenregierung. Es ist eine ideale Zertifizierung für Fachleute, die im Bereich des Datenmanagements arbeiten und für die Sicherstellung der Datenqualität in ihren Organisationen verantwortlich sind.
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Die DAMA DQ-1220-Zertifizierungsprüfung ist ideal für Datenqualitätsprofis, Datenanalysten, Datenwissenschaftler und andere Fachkräfte, die im Bereich Datenmanagement arbeiten. Es ist auch für Personen geeignet, die ihre Fähigkeiten und ihr Wissen im Datenqualitätsmanagement verbessern möchten. Die Prüfung ist offen für alle mit relevanten Erfahrung und Kenntnissen, und es gibt keine Voraussetzungen für die Prüfung.
35. Frage
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
Antwort: B
Begründung:
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.
36. Frage
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?
Antwort: B
Begründung:
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.
37. Frage
Data Staging areas are populated from source databases using extract functions, transformed and then:
Antwort: A
Begründung:
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.
38. Frage
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?
Antwort: B
Begründung:
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.
39. Frage
A Data Quality team has identified 500 defects across several domains. Which factor should have the strongest influence on remediation priority?
Antwort: B
Begründung:
Remediation should principally be prioritized according to business impact and risk. Not all defects have equivalent consequences, even when they occur at similar frequencies.
A defect affecting regulatory reporting, customer payments, safety-critical operations, executive reporting, or high-value master data may require immediate remediation. A larger number of defects affecting a low- impact optional field may legitimately receive lower priority.
A robust prioritization model may consider financial loss, regulatory exposure, operational disruption, customer impact, reputational damage, number of dependent systems, recurrence rate, remediation cost, and whether a Critical Data Element is involved.
This risk-based approach prevents Data Quality programs from becoming simple defect-count reduction exercises. The objective is not merely to maximize the number of corrected records but to improve fitness for purpose where poor data creates material consequences.
Governance should approve prioritization criteria and resolve conflicts where different business areas assign different importance to the same issue. Metadata and lineage provide evidence about downstream dependencies and affected processes.
DAMA's revision of Chapter 13 adds a clearer Critical Data Element concept and clarifies responsibility within the Data Quality Improvement Lifecycle, reinforcing risk-based prioritization.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Issue Prioritization; Business Impact; Critical Data Elements; Risk; Remediation.
40. Frage
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