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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Topic 2: Data Quality Improvement | 20% | - Data Cleansing Techniques - Validation Rules - Standardization - Data Enrichment |
| Topic 3: Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Continuous Monitoring - Policies and Procedures - Data Stewardship |
| Topic 4: Data Quality Assessment and Measurement | 25% | - Profiling Techniques - Benchmarking - Root Cause Analysis - Metrics and KPIs |
| Topic 5: Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - ISO 8000 (Data Quality) - Other Industry Standards |
>> DQ-1220 Prüfungsmaterialien <<
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76. Frage
A Data Quality rule has been implemented and the defect rate has fallen below the agreed threshold. What activity should continue?
Antwort: D
Begründung:
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.
77. 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.
78. Frage
The creation of overly complex enterprise integration over time is often a symptom of:
Antwort: B
Begründung:
The strongest indicator is the uncontrolled proliferation of multiple integration technologies. Enterprise integration environments often become progressively more complex when different projects independently adopt different ETL platforms, messaging products, APIs, middleware technologies, replication mechanisms, file-transfer approaches, and proprietary interfaces.
The problem is architectural fragmentation. Each integration technology introduces its own configuration methods, transformation logic, monitoring mechanisms, metadata, failure handling, security controls, support skills, and operational procedures. As the number of technologies increases, point-to-point dependencies multiply and the organization accumulates technical debt. This makes end-to-end lineage, troubleshooting, change-impact analysis, and consistent application of Data Quality rules significantly harder. The source question itself identifies this specific integration-management issue. DAMA-oriented exam material likewise identifies multiple integration technologies as the characteristic cause of an overly complex integration landscape.
DAMA-DMBOK2 therefore emphasizes managed Data Integration and Interoperability architecture, reusable patterns, common models, governed interfaces, and metadata describing transformations.
From a Data Quality perspective, uncontrolled integration technology can result in inconsistent transformations, duplicated cleansing rules, mismatched reference values, and poorly understood lineage.
Standardization reduces these risks by making movement and transformation processes more transparent and governable.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Integration Architecture; Metadata and Lineage; Chapter 13 - Quality Controls Across Data Movement.
79. Frage
The main difference between a System of Record and a System of Reference is:
Antwort: B
Begründung:
DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption.
A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.
Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.
This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.
Option D is incorrect because "System of Record" and "System of Reference" do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.
Reference Topics: DAMA-DMBOK2 Chapter 10 - System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 - Data Quality and Reference/Master Data Management.
80. Frage
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Antwort: A
Begründung:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
81. Frage
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