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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Metrics and KPIs - Benchmarking - Profiling Techniques |
| Topic 2: Data Quality Improvement | 20% | - Standardization - Data Enrichment - Data Cleansing Techniques - Validation Rules |
| Topic 3: Data Quality Governance | 15% | - Policies and Procedures - Continuous Monitoring - Data Quality Roles and Responsibilities - Data Stewardship |
| Topic 4: Data Quality Frameworks and Standards | 20% | - Other Industry Standards - DAMA DMBOK Framework - ISO 8000 (Data Quality) |
| Topic 5: Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
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NEW QUESTION # 117
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
Answer: D
Explanation:
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
NEW QUESTION # 118
The implementation of a 'Super Type - Sub Type' structure can use the following 2 options:
Answer: D
Explanation:
DAMA-DMBOK2 identifies two recognized approaches for resolving logical supertype-subtype abstractions when moving into physical database design: Subtype Absorption and Supertype Partition.
With Subtype Absorption, attributes belonging to subtype entities are incorporated into the table representing the supertype. Attributes that apply only to particular subtypes may consequently be nullable. This approach reduces the number of physical tables but can introduce sparsity and requires explicit rules to ensure subtype- specific attributes remain semantically valid.
With Supertype Partition, the attributes belonging to the supertype are carried into separate physical tables representing each subtype. This can simplify subtype-specific processing but introduces duplication of common structures and requires disciplined metadata and modelling control.
The choice has direct consequences for Data Quality. Subtype absorption requires validity and completeness rules to distinguish legitimate nulls from missing data. Supertype partition requires consistency controls to ensure shared attributes retain identical definitions and constraints across subtype tables. Metadata repositories should document subtype discriminators, attribute definitions, constraints, and inheritance rules.
DMBOK2 therefore treats the transformation as a deliberate physical modelling decision rather than the generic "merge/split" terminology presented in the distractors.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Physical Data Modeling; Resolve Logical Abstractions; Chapter 13 - Validity, Completeness and Consistency; Metadata Management.
NEW QUESTION # 119
A Data Quality score is calculated from Completeness, Accuracy, and Timeliness. Why might the dimensions be weighted differently?
Answer: C
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 # 120
A CRM contains separate records for "Jonathan Smith", "Jon Smith", and "J. Smith". Investigation confirms that all three records represent the same customer. Which Data Quality dimension is primarily affected?
Answer: D
Explanation:
The primary issue is Uniqueness because more than one record represents the same real-world entity.
Uniqueness evaluates whether unintended duplicate instances exist within a dataset or across integrated datasets.
Duplicate customer records can arise because of spelling variation, abbreviations, different contact details, weak matching rules, or inconsistent identifier capture. Exact duplicate detection alone is therefore insufficient. Effective entity resolution frequently requires probabilistic or deterministic matching across names, addresses, identifiers, telephone numbers, and other attributes.
DAMA-aligned Data Quality frameworks define uniqueness in terms of avoiding duplicate representation of the same entity. They also recognize that duplicate records may differ in individual attribute values while still representing the same person or object.
The issue is closely related to Master Data Management. An MDM process may match the three source records, establish that they belong to one customer, and apply survivorship rules to construct an authoritative master representation.
The correct remediation should also address the source process that allowed duplicates to be created; otherwise cleansing will merely remove symptoms while new duplicates continue to appear.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Uniqueness; Matching; De-duplication; Chapter 10 - Master Data Management; Entity Resolution.
NEW QUESTION # 121
What is the final step in the development of a business-data-driven roadmap?
Answer: E
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 # 122
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