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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle - Definition of Data Quality |
| Topic 2: Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Continuous Monitoring - Data Stewardship - Policies and Procedures |
| Topic 3: Data Quality Improvement | 20% | - Data Enrichment - Validation Rules - Data Cleansing Techniques - Standardization |
| Topic 4: Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - Other Industry Standards - ISO 8000 (Data Quality) |
| Topic 5: Data Quality Assessment and Measurement | 25% | - Benchmarking - Metrics and KPIs - Root Cause Analysis - Profiling Techniques |
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NEW QUESTION # 13
Three common interaction models for data integration are:
Answer: A
Explanation:
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
NEW QUESTION # 14
The best way to manage a data architecture roadmap is by using:
Answer: C
Explanation:
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.
NEW QUESTION # 15
The DAMA Wheel contains:
Answer: C
Explanation:
The DAMA Wheel represents the principal Data Management Knowledge Areas defined by DAMA- DMBOK2. Data Governance occupies the central position because governance provides authority, coordination, standards, and oversight across the other disciplines. The surrounding areas include Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, and Data Quality. DAMA descriptions of the framework identify these as Knowledge Areas rather than project deliverables or maturity dimensions.
The Wheel is deliberately integrative. No Data Management discipline operates effectively in complete isolation. For example, Data Quality relies on Governance for authority and accountability, Metadata Management for definitions and lineage, Reference and Master Data for consistent shared entities and codes, Data Architecture for structural context, and Integration for controlled movement between systems.
This interaction is central to Chapter 13. Data Quality cannot be reduced to cleansing defective records after problems appear. Sustainable quality requires coordinated management throughout the lifecycle and across the other DAMA knowledge areas.
Data management processes, deliverables, strategic initiatives, and maturity assessments are all legitimate DAMA concepts, but they are not what the DAMA Wheel itself principally represents.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; DAMA Wheel; Knowledge Areas; Chapter 3 - Governance; Chapter 13 - Data Quality Relationships with Other Knowledge Areas.
NEW QUESTION # 16
When a data quality team has more issues than they can manage, they should look to:
Answer: E
Explanation:
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
NEW QUESTION # 17
Who should normally define the business meaning and acceptable quality requirements for a critical customer attribute?
Answer: D
Explanation:
A Business Data Steward, working with relevant business stakeholders and governance bodies, is the appropriate role to define or coordinate business meaning and quality expectations.
Quality requirements cannot be derived solely from database structures. The organization must understand what the attribute means, how it is used, what values are acceptable, which source is authoritative, and what consequences arise when it is wrong.
Data Stewards bridge business knowledge and formal Data Management controls. They commonly participate in maintaining glossary definitions, defining business rules, resolving issues, clarifying ownership, and establishing Data Quality expectations.
Technical specialists contribute implementation knowledge. A DBA may identify datatype constraints, while an integration specialist can implement transformations. Neither should independently determine business meaning.
DAMA's public framework describes Metadata Management as supporting definitions, lineage, and governance, while Reference and Master Data Management ensures consistency in shared core entities.
The strongest operating model therefore combines business accountability with technical execution.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Chapter 11 - Business Metadata; Chapter 13 - Data Quality Requirements; Critical Data Elements.
NEW QUESTION # 18
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