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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality tools and technologies - Data quality in lifecycle management |
| Topic 2: Data Quality Improvement | 20% | - Root cause analysis - Monitoring and continuous improvement - Data cleansing and standardization |
| Topic 3: Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Data profiling techniques - Assessment methods and tools |
| Topic 4: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Integration with data governance - Data quality strategy and policy |
| Topic 5: Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
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NEW QUESTION # 15
A Data Quality score is calculated from Completeness, Accuracy, and Timeliness. Why might the dimensions be weighted differently?
Answer: B
Explanation:
Dimensions may be weighted differently because their business impact is not necessarily equal for a particular dataset or process.
For an emergency-response system, Timeliness may be more critical than completeness of optional descriptive fields. For regulatory reporting, Accuracy and Integrity may carry substantially greater risk than minor delays. For a marketing contact list, completeness and currency may dominate.
A composite Data Quality score should therefore reflect business priorities rather than assuming that each dimension contributes identical value. Weighting must be transparent and governed; otherwise a high score in low-impact dimensions could hide serious failure in a critical one.
The first step remains defining business requirements and Critical Data Elements. Once those are established, dimensions, thresholds, and weights can be linked to documented business consequences.
A score should also retain underlying component metrics. A single aggregate percentage is insufficient for diagnosis because two datasets with the same overall score may have very different risk profiles.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Metrics; Scorecards; Business Impact; Critical Data Elements; Quality Dimensions; Thresholds.
NEW QUESTION # 16
What is one of the Data Architecture artifacts which are usually captured within development projects, and then standardised and managed by data architects?
Answer: E
Explanation:
The correct answer is Data models. DAMA-DMBOK2 explicitly explains that data models and other Data Architecture artifacts are commonly created or captured within development projects and are subsequently standardized and managed by Data Architects.
Development projects routinely produce conceptual, logical, and physical representations of the data required by a solution. If each project develops those structures independently without architectural review, inconsistent terminology, duplicated entities, conflicting definitions, and incompatible relationship structures can accumulate across the enterprise. Data Architects therefore review project models, reconcile them with enterprise standards, and identify structures suitable for reuse.
An Enterprise Data Model is itself an important architectural artifact, but the wording of the question refers specifically to artifacts typically generated in individual development projects and later standardized. The DMBOK2 passage uses data models in precisely that context.
This process has a direct Data Quality benefit. Standardized models improve consistency of definitions, domains, keys, relationships, and constraints. They also provide metadata from which integrity, uniqueness, validity, and completeness rules can be derived.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Architectural Artifacts; Development Projects; Chapter 5 - Data Modeling and Design; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 17
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: A
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 18
A Data Quality rule states that every active supplier must have a valid tax identifier. Who should approve whether the rule is mandatory for the Supplier domain?
Answer: A
Explanation:
Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.
A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.
The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.
This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.
DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance and Stewardship; Chapter 13 - Data Quality Rules; Metadata Management; Supplier Master Data.
NEW QUESTION # 19
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: C
NEW QUESTION # 20
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