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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Governance | 15% | - Continuous Monitoring - Policies and Procedures - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
| Data Quality Improvement | 20% | - Standardization - Validation Rules - Data Cleansing Techniques - Data Enrichment |
| Data Quality Frameworks and Standards | 20% | - Other Industry Standards - DAMA DMBOK Framework - ISO 8000 (Data Quality) |
| Data Quality Assessment and Measurement | 25% | - Metrics and KPIs - Root Cause Analysis - Benchmarking - Profiling Techniques |
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NEW QUESTION # 84
A Data Quality rule has been implemented and the defect rate has fallen below the agreed threshold. What activity should continue?
Answer: B
Explanation:
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.
NEW QUESTION # 85
Critical to the incremental development of the data warehouse is:
Answer: B
Explanation:
A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA- DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.
A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.
This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures.
Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.
Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.
Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.
Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence - Maintain Data Products; Release Management; Incremental Development; Chapter 13 - Quality Monitoring and Change Control.
NEW QUESTION # 86
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?
Answer: C
Explanation:
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.
NEW QUESTION # 87
The primary reason for identifying Critical Data Elements is to:
Answer: D
Explanation:
Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.
Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.
The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.
Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.
Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.
Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.
NEW QUESTION # 88
Data modelling tools and model repositories are necessary for:
Answer: B
Explanation:
DAMA-DMBOK2 states that Data Modeling tools and model repositories are necessary for managing the Enterprise Data Model at all levels. This includes maintaining relationships among conceptual, logical, and physical representations and controlling how models created for different purposes fit into the wider enterprise architecture.
An enterprise modeling repository provides more than diagramming. It supports version control, definitions, lineage between model layers, comparison of changes, reuse of standard entities and attributes, enforcement of modelling conventions, and communication across projects. Many modelling tools also provide relationship and lineage capabilities that enable architects to trace how concepts represented at a high level are implemented in detailed models.
Option C describes one important capability-visualizing and communicating database designs-but it is narrower than the DMBOK2 purpose stated in the question. Similarly, modelling tools support governance and architectural visualization but are not solely established for either function.
For Data Quality, governed models provide the structural metadata from which rules can be derived. Keys support uniqueness; relationships support integrity; domains support validity; mandatory attributes support completeness; and standardized definitions support consistency.
The repository therefore becomes an important bridge between Data Architecture, Metadata Management, Data Governance, and Data Quality.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling Tools and Repositories; Enterprise Data Model; Conceptual/Logical/Physical Models; Chapter 13 - Structural Quality Rules.
NEW QUESTION # 89
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