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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
| Topic 2: Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
| Topic 3: Data Quality Concepts and Principles | 15% | - Data quality dimensions - Definition and importance of data quality - Data quality in DMBOK framework |
| Topic 4: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Topic 5: Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
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55. Frage
A report value is incorrect, and the analyst needs to identify every source field and transformation used to calculate it. Which capability is most important?
Antwort: D
Begründung:
The required capability is Data Lineage. Lineage documents how data originates, moves, transforms, and ultimately appears in downstream reports, applications, and analytical products.
When a report value is wrong, lineage allows investigators to trace the value backwards through calculations, mappings, staging structures, integration processes, source tables, and potentially external providers. This prevents teams from guessing where the defect originated.
Metadata Management is the principal discipline responsible for collecting and maintaining this contextual information. DAMA's public framework identifies lineage and definitions as central Metadata Management capabilities.
Lineage is also essential for change-impact analysis. If a source element changes, teams can determine which reports, models, or applications depend upon it.
For Data Quality, lineage supports root-cause analysis and coordinated remediation. A downstream value may be accurate relative to the transformation applied but still wrong because the source or transformation logic was incorrect.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Source-to-Target Mapping; Root-Cause Analysis; Data Integration.
56. Frage
A pensioner who usually receives a quarterly bill of around $300 was sent a $100,000,000 electricity bill.
They were a victim of poor data quality checks in which dimension?
Antwort: B
Begründung:
The failed control is principally Reasonableness. DAMA treats reasonableness as the extent to which data values or patterns conform to credible expectations. The current DMBOK2 revision recognizes Reasonableness among the standard Data Quality dimensions, alongside Accuracy, Validity, Completeness, Integrity, Uniqueness, Timeliness, Currency and Consistency.
A $100,000,000 residential electricity bill may be syntactically valid: the field may accept the number, the record may satisfy referential constraints, and the value may have arrived on time. Nevertheless, it is grossly inconsistent with the customer's historical pattern of approximately $300 per quarter. A reasonableness control would therefore compare the amount with expected ranges, historical consumption, statistical limits, peer-group distributions, or business thresholds and flag the value before billing.
This illustrates an important distinction from Accuracy. Accuracy asks whether the stored value correctly represents reality. Reasonableness asks whether the value is credible in context. The absurd magnitude itself is detectable without first obtaining an independently verified "true" bill value.
Practical controls include range checks, deviation-from-history rules, z-score/outlier detection, control limits, and exception thresholds. Such rules should be governed as metadata and owned by an appropriate Data Steward.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Dimensions; Reasonableness; Measurement and Monitoring; Statistical Process Control; Data Quality Rules.
57. Frage
Project costs will increase faster by adding to the data model than adding to the process model because:
Antwort: B
Begründung:
Each additional data item creates obligations across multiple lifecycle processes, which is why option D is the most complete answer. DAMA-DMBOK2 treats data as an asset that must be managed throughout an interconnected lifecycle rather than merely stored in a database. Activities include creation or acquisition, validation, integration, transformation, storage, maintenance, access, use, protection, retention, archiving, and disposal.
Consequently, introducing another meaningful data element may require changes to data models, metadata repositories, interfaces, mappings, business rules, validation controls, security classifications, quality measurements, lineage documentation, reporting logic, retention rules, and stewardship responsibilities. The cost is therefore cumulative across several management processes.
Option A identifies only migration. Option B reduces the impact to storage, which is usually a minor component. Option C applies only where a warehouse is involved. Option E identifies one legitimate consideration-security-but security is only one part of the complete management obligation.
The Data Quality implication is substantial. Every additional Critical Data Element may require explicit definitions of validity, completeness, accuracy, timeliness, and consistency, together with monitoring and issue-management procedures. This demonstrates why organizations should avoid collecting data indiscriminately and should manage data according to genuine business value and requirements.
Reference Topics: DAMA-DMBOK2 Chapter 1 - Data Lifecycle; Data as an Asset; Chapter 5 - Data Modeling; Chapter 13 - Data Quality Requirements and Monitoring.
58. Frage
The best way to manage a data architecture roadmap is by using:
Antwort: B
Begründung:
Integrated strategic reviews provide the strongest mechanism for managing a Data Architecture roadmap because the roadmap must remain synchronized with enterprise strategy, business capability priorities, other architecture domains, projects, resources, and changing dependencies.
DAMA-DMBOK2 describes the Enterprise Data Architecture roadmap as the three-to-five-year path by which the target architecture becomes reality. Crucially, it states that this roadmap must be integrated into the overall Enterprise Architecture roadmap, including milestones, required resources, cost estimates, and business-capability workstreams. The roadmap should also reflect business requirements, current conditions, technical assessments, and organizational maturity.
This means roadmap management cannot sensibly be reduced to a once-a-year exercise. Strategic conditions, technology decisions, project sequencing, dependencies, and regulatory requirements may change throughout the roadmap period. Integrated reviews allow those changes to be evaluated in the context of the wider enterprise architecture rather than independently.
Senior-management support is necessary for authority and funding, while peer reviews and results evaluation are useful control activities. None, however, provides the same integrated strategic mechanism for keeping architectural direction aligned with enterprise priorities.
From a Data Quality perspective, such reviews also ensure that architecture changes preserve authoritative sources, lineage, integration controls, and enterprise quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Develop a Roadmap; Enterprise Architecture Integration; Data Dependencies; Lifecycle Reviews; Architecture Governance.
59. Frage
Emergency contact phone number would be found in which master data management program?
Antwort: C
Begründung:
An emergency contact phone number belongs to the Employee master-data domain. Employee Master Data describes relatively stable information about employees that needs to be consistently identified and reused across organizational processes and systems. Contact details associated with an employee-including emergency-contact information-therefore fall naturally within an Employee MDM program rather than Product or Asset MDM.
The broader DAMA Master Data framework identifies employees as one of the recurring enterprise entities that may require coordinated management alongside customers, products, suppliers, locations, and similar core subjects. Published versions of this specific DAMA certification item likewise identify Employee as the correct domain.
Managing this information centrally or through coordinated master-data processes helps prevent conflicting phone numbers, obsolete emergency contacts, duplicate employee identities, and inconsistent values across Human Resources and other authorized systems.
The data is also sensitive personal information. Governance should therefore define ownership, access rights, retention rules, and permitted usage. Quality controls should address completeness, validity, currency, and accuracy, because outdated emergency-contact information may fail precisely when it is needed.
Metadata Management should record definitions and classifications so that "emergency contact phone number" is consistently distinguished from an employee's own mobile or work telephone number.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Domains; Employee Master Data; Data Governance; Chapter 13 - Accuracy, Completeness and Currency.
60. Frage
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