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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Integration with data governance - Roles and responsibilities - Data quality strategy and policy |
| Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Business value and ROI - Data quality in lifecycle management |
| Data Quality Improvement | 20% | - Data cleansing and standardization - Monitoring and continuous improvement - Root cause analysis |
| Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Data profiling techniques - Defining quality rules and metrics |
| Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Definition and importance of data quality - Data quality dimensions |
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NEW QUESTION # 91
Critical Data is most often used in:
Answer: E
Explanation:
All of these is correct because DAMA-DMBOK2 defines criticality according to the business use and impact of the data rather than by a particular technical characteristic. Critical Data Elements are commonly associated with regulatory, financial, or management reporting; operational business needs; measurement of product quality and customer satisfaction; and strategic initiatives such as competitive differentiation. DAMA's revised Chapter 13 also explicitly adds and clarifies the concept of Critical Data Elements within the Data Quality framework.
The purpose of identifying critical data is prioritization. Organizations cannot apply identical levels of profiling, monitoring, stewardship, remediation, and control to every data element. Data whose failure could create substantial operational, financial, legal, regulatory, customer, or reputational impact receives greater management attention.
For example, values feeding regulatory reports require stringent accuracy and traceability; operational data may require high availability and timeliness; customer-satisfaction measures require reliable and consistent inputs; and strategically important data can affect competitive decisions. Current DAMA-aligned material confirms these same usage categories for Critical Data Elements.
Once Critical Data Elements are identified, they should be linked to business definitions, Data Owners and Stewards, quality dimensions, measurable rules, lineage, authoritative sources, thresholds, and issue- management processes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Business Drivers; Data Quality Requirements; Prioritization; Data Governance; Metadata Management.
NEW QUESTION # 92
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?
Answer: A
Explanation:
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.
NEW QUESTION # 93
A retail system accepts an order for 750,000 identical office chairs from an individual consumer. The value passes all datatype, domain, and mandatory-field checks. Which additional Data Quality dimension should detect the anomaly?
Answer: A
Explanation:
The appropriate dimension is Reasonableness. A value may comply with technical and business-domain constraints while still being implausible in its operating context. An individual customer purchasing 750,000 office chairs is technically possible, but it is sufficiently unusual that it should trigger review.
Reasonableness controls evaluate whether values and combinations of values fall within credible expectations. These controls often depend on business context, historical behaviour, peer comparisons, statistical limits, or relationships between multiple fields.
The current DMBOK2 revision standardizes the term Reasonableness within its nine standard dimensions.
The broader DMBOK2 dimension framework links reasonableness to whether data should be regarded as credible within the operational context.
Useful controls might compare order quantity against customer type, historical maximums, product availability, typical basket size, or statistical deviation from normal purchasing behaviour.
Reasonableness rules are especially valuable for identifying errors that conventional validation cannot detect.
However, unusual data should not automatically be treated as incorrect; it should usually be flagged for investigation.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Reasonableness; Statistical Validation; Exception Detection; Business Rules.
NEW QUESTION # 94
What is the final step in the development of a business-data-driven roadmap?
Answer: C
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 95
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Answer: B
Explanation:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
NEW QUESTION # 96
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