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Die DAMA DQ-1220 Zertifizierungsprüfung wird weltweit als Standard zur Messung der Kompetenz von Datenqualitätsprofis anerkannt. Es handelt sich um eine umfassende Prüfung, die das Verständnis des Kandidaten für die Grundsätze, Techniken und Methoden des Datenqualitätsmanagements bewertet. Die Prüfung ist darauf ausgelegt, die Fähigkeit des Kandidaten zur Identifizierung und Behebung von Datenqualitätsproblemen, zur Entwicklung und Umsetzung von Datenqualitätsplänen und zur Messung der Wirksamkeit von Datenqualitätsinitiativen zu bewerten. Das Bestehen der DAMA DQ-1220 Zertifizierungsprüfung zeigt, dass der Kandidat über das notwendige Wissen und die Fähigkeiten verfügt, um sicherzustellen, dass die Daten einer Organisation genau, zuverlässig und vertrauenswürdig sind.
Die DAMA DQ-1220 Zertifizierungsprüfung ist ein führendes Akkreditierungsprogramm, das für Datenmanagement-Profis entwickelt wurde, die ihr Wissen und ihre Fähigkeiten im Bereich der Datenqualitätsverwaltung verbessern möchten. Die Zertifizierungsprüfung wird von der Data Management Association (DAMA) International angeboten, einer globalen gemeinnützigen Organisation, die die Entwicklung und Fortschritte von Datenmanagement-Praktiken und -Profis fördert. Die Prüfung ist speziell darauf ausgelegt, das Verständnis von grundlegenden Prinzipien, Methoden und Best Practices in der Datenqualitätsverwaltung sowie die Fähigkeit einer Person, dieses Wissen auf reale Szenarien anzuwenden, zu bewerten.
>> DQ-1220 Zertifizierungsfragen <<
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Die DAMA DQ-1220-Zertifizierungsprüfung richtet sich an Personen, die ihre Fachkenntnisse im Datenqualitätsmanagement nachweisen möchten. Die Prüfung deckt eine breite Palette von Themen in Bezug auf die Datenqualität ab, einschließlich Datenprofilerierung, Datenreinigung, Datenvalidierung und Datenverwaltung. Diese Zertifizierung ist ideal für Fachleute, die in Datenmanagement, Datenanalyse oder Business Intelligence arbeiten.
112. Frage
Which of the following is a directive that codifies principles and management intent into fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information?
Antwort: C
Begründung:
The definition describes Data Policies. DAMA-DMBOK2 states that data policies are directives that translate organizational principles and management intent into fundamental rules controlling how data is created, acquired, protected, maintained, assured for quality, and used.
The distinction between governance and policy is important. Data Governance is the broader authority and decision-making framework. Policies are one of the principal instruments produced or sponsored through that framework. They define what must or must not occur. Standards and procedures subsequently translate those policies into specific implementation requirements and operational methods.
For Data Quality, a policy might require business-critical data to have designated ownership, documented definitions, measurable quality rules, monitored thresholds, and formal remediation processes. The detailed thresholds themselves may reside in standards or rule repositories rather than in the high-level policy.
Metadata Management makes policy operational by documenting definitions, classifications, ownership, lineage and applicable quality rules. Master Data Management applies those policies to shared business entities and controlled reference values. Data Stewards then monitor compliance and escalate violations through governance mechanisms.
Data asset valuation is concerned with determining economic value, while Data Management encompasses the complete discipline. Neither provides the specific "directive" definition stated in the question.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Policies; Governance Principles; Standards and Procedures; Chapter 13 - Data Quality Governance; Metadata and Master Data interactions.
113. Frage
What is a steward?
Antwort: E
Begründung:
In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: "A steward is a person whose job it is to manage the property of another person." It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.
A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.
The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.
A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 - Data Quality Governance and Responsibilities.
114. Frage
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?
Antwort: D
Begründung:
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.
115. Frage
Big data management requires:
Antwort: A
Begründung:
DAMA-DMBOK2 establishes the guiding principle that Big Data management requires more discipline than relational data management. The reason is not simply data volume. Big Data environments combine large volumes with substantial variation in structure, source, velocity, semantics, and reliability. These characteristics increase the probability of uncontrolled duplication, inconsistent interpretation, poor provenance, inappropriate use, and undetected quality defects. DAMA's Big Data guidance specifically emphasizes disciplined management of metadata describing Big Data sources, their origins, and their value.
From a Data Quality perspective, scale does not make data fit for purpose. Large datasets still require defined quality expectations, profiling, monitoring, lineage, controlled transformations, ownership, and governance.
Metadata becomes particularly important because analysts must understand where datasets originated, how they were transformed, their meaning, and whether they are appropriate for a particular analytical objective.
Data Governance must consequently establish accountability, acceptable-use policies, security requirements, quality expectations, and escalation mechanisms. Statistical and automated controls are also essential because manual inspection becomes impractical at Big Data scale.
Neither larger budgets nor data-science certification substitutes for sound data-management discipline.
Likewise, reducing controls because data is large creates greater-not lower-operational and analytical risk.
Reference Topics: DAMA-DMBOK2 - Big Data and Data Science; Big Data Management Principles; Metadata Management; Data Governance; Chapter 13 - Fitness for Purpose and Data Quality Controls.
116. Frage
A business case for adding a new master data management solution is dependent on achieving greater value from:
Antwort: C
Begründung:
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.
117. Frage
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