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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Topic 2: Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Topic 3: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Topic 4: Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Topic 5: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
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NEW QUESTION # 19
Three source systems provide different telephone numbers for the same customer. An MDM hub selects one number according to approved source-priority rules. This is an example of:
Answer: A
Explanation:
The process is Survivorship. In Master Data Management, survivorship determines which value should become the preferred or authoritative representation when multiple source records contain conflicting values for the same attribute.
Rules may prioritize particular systems, use recency, trust scores, verification status, completeness, or combinations of factors. For example, a verified customer-service update might outrank an older marketing- system telephone number.
Survivorship occurs after or in conjunction with matching and entity resolution. The organization first determines that records from different sources represent the same real-world customer and then determines which attribute values should populate the mastered representation.
The process requires governance because the "best" value is a business decision, not merely a technical one.
Data Stewards should approve the rules, and metadata should record source priority, rule logic, and lineage.
DAMA's framework positions Reference and Master Data Management as the discipline responsible for ensuring consistent core entities across the organization.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Authoritative Values; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 20
A source application is modified so that a mandatory product identifier can no longer be left blank. Which type of Data Quality action is this?
Answer: C
Explanation:
The application change is a preventive Data Quality control because it prevents a known defect from being created in the first place.
A mandatory-field control ensures that records cannot be accepted without the required Product Identifier.
This differs from a detective control, which would identify missing identifiers after records had already entered the system.
Preventive controls are generally preferable where the organization controls the point of data creation and the business rule is sufficiently clear. They reduce downstream cleansing, exception handling, reconciliation, and operational rework.
However, making the field technically mandatory is appropriate only if the business requirement genuinely applies to every relevant record. Governance and stewardship should confirm the rule before implementation.
If certain product categories legitimately lack the identifier, the validation should incorporate those conditions instead of enforcing an overly broad requirement.
The Product Identifier may also link the transaction to governed Product Master Data, making integrity and consistency important alongside completeness.
DAMA's Data Quality framework emphasizes defining rules, detecting defects, implementing improvement, and integrating quality with Governance and Master Data disciplines rather than relying solely on after-the- fact cleansing.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Completeness; Data Quality Improvement Lifecycle; Chapter 10 - Product Master Data; Governance.
NEW QUESTION # 21
A dataset contains every required customer record, but 8% of the telephone numbers belong to different people. Which statement is correct?
Answer: B
Explanation:
The dataset can be complete while remaining inaccurate. Completeness measures whether the required records and values are present; Accuracy measures whether those values correctly represent reality.
If every customer has a telephone number, the field may achieve 100% completeness. However, if 8% of those numbers belong to other people, the values are inaccurate.
This distinction is one of the most important principles in Data Quality measurement. A populated field is not automatically trustworthy. Similarly, a syntactically valid value is not necessarily accurate.
DAMA-aligned guidance explicitly distinguishes these dimensions and notes that a complete dataset may still contain incorrect values.
The appropriate quality scorecard should therefore present completeness and accuracy separately rather than treating one as evidence of the other.
For high-risk customer contact processes, the business may define independent thresholds for both dimensions. Verification services, customer confirmation, authoritative sources, and exception handling may be needed to improve accuracy after basic completeness has already been achieved.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Accuracy; Data Quality Dimensions; Metrics; Fitness for Purpose.
NEW QUESTION # 22
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 # 23
Reference data is often a list of code values with their full names. One example is:
Answer: C
Explanation:
Country codes associated with country names are a classic example of Reference Data. DAMA-DMBOK2 defines Reference Data as data used to characterize or classify other data or to relate organizational data to externally defined information. The simplest reference-data structure consists of a code and its corresponding description. DMBOK2 specifically uses geographic and standards-based examples, including country codes such as DE, US, and TR.
A code such as GB therefore acts as a standardized machine-processable value, while "United Kingdom" supplies the human-readable meaning. Such code lists are reused across applications, integrations, reporting platforms, Master Data systems, and analytical environments.
Reference Data quality is important because inconsistent code sets can create widespread downstream defects.
If different systems use incompatible country codes, the organization may experience failed integrations, incorrect aggregation, duplicate mappings, reporting discrepancies, and inconsistent interpretation. Data Governance should therefore establish authoritative sources and stewardship responsibilities for important reference domains.
Metadata Management records the meaning, provenance, permitted values, relationships, and mappings between code sets. Master Data Management frequently consumes these governed codes to classify master entities such as customers, suppliers, products, and locations.
The other choices describe relationships or transformations, but they do not represent the straightforward code- value/description list structure identified by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data; Reference Lists; Code Sets; Authoritative Sources; Chapter 13 - Validity, Consistency and Integrity.
NEW QUESTION # 24
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