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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Concepts and Principles | 15% | - Data quality dimensions - Data quality in DMBOK framework - Definition and importance of data quality |
| Topic 2: Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Data quality tools and technologies - Business value and ROI |
| Topic 3: Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Integration with data governance - Roles and responsibilities |
| Topic 4: Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Assessment methods and tools - Defining quality rules and metrics |
| Topic 5: Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
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NEW QUESTION # 96
A customer database contains postal addresses but lacks latitude and longitude. The organization adds geographic coordinates obtained from a trusted external service. This activity is an example of:
Answer: B
Explanation:
This is Data Enrichment. Enrichment enhances existing records by adding useful information derived from internal or external sources. The original postal address remains intact, while geographic coordinates are appended to make the customer record more useful for location analysis, logistics, service coverage, route planning, or segmentation.
Enrichment differs from cleansing. Cleansing corrects or standardizes defective data; enrichment adds information that was not previously present. A process may perform both-for example, standardizing an address before passing it to a geocoding service.
External enrichment introduces governance and quality considerations. The organization should assess source reliability, licensing and permitted usage, refresh frequency, match confidence, geographical precision, lineage, and whether enrichment values should be treated as authoritative or derived.
Metadata should record that latitude and longitude were obtained from an external geocoding process rather than directly supplied by the customer. Quality metrics may measure match rate, confidence, completeness, or positional accuracy.
DAMA identifies Data Quality Management as including error detection, cleansing, and enrichment strategies, while Metadata Management supports understanding of lineage, definitions, and usage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Enrichment; Cleansing; Profiling; Metadata Lineage; External Data Sources.
NEW QUESTION # 97
Integrating data security with document and content management knowledge areas, guides the implementation of:
Answer: A
Explanation:
Document and Content Management focuses on information stored outside conventional relational databases, including documents, images, multimedia, email, and other semi-structured or unstructured assets. Integrating this knowledge area with Data Security therefore guides the implementation of appropriate access and authorization controls for unstructured data.
DAMA's framework treats security as a cross-cutting discipline rather than something applicable only to database tables. Organizational documents can contain personally identifiable information, intellectual property, contracts, financial records, or other confidential material and therefore require the same disciplined approach to authentication, authorization, classification, retention, and monitoring as structured data. DAMA- aligned references explicitly identify appropriate access and authorization to unstructured data as the relevant interaction between these knowledge areas.
Metadata is also important because document classifications, ownership, retention category, confidentiality level, and permitted audiences provide the information needed to enforce controls.
Option A refers to structured data and therefore misses the specific contribution of Document and Content Management. Fitness-for-purpose measurement belongs primarily to Data Quality, while data-mart privacy addresses a narrower structured analytical environment.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Chapter 9 - Document and Content Management; Unstructured Data; Access Control; Authorization; Information Classification.
NEW QUESTION # 98
The loading of country codes into a CRM is a classic:
Answer: B
Explanation:
Loading standardized country codes into a CRM is a classic example of Reference Data Integration. Country codes constitute controlled reference values used to classify or characterize other data. They differ from transactional information such as orders and payments and from master entities such as individual customers or products.
DAMA treats countries, currencies, geographic classifications, status codes, and similar controlled domains as typical Reference Data. The uploaded question therefore tests the distinction between integrating reusable code sets and integrating Master or Transaction Data. Published versions of the same DAMA item confirm Reference Data Integration as the intended answer.
In practice, an authoritative reference source may provide standardized country identifiers to CRM, ERP, MDM, analytical, and integration systems. Central management prevents different applications from independently inventing or changing equivalent codes.
This is also a Data Quality control. Reference integration improves Validity by restricting values to approved domains and Consistency by ensuring that different systems interpret equivalent countries identically. For example, an uncontrolled mixture of GB, UK, 826, and proprietary internal codes may create integration and reporting errors unless mappings are formally governed.
Metadata should document the authoritative source, code meaning, effective dates, mappings, ownership, and applicable standards.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference Data; Code Sets; Reference Data Integration; Authoritative Sources; Chapter 13 - Validity and Consistency.
NEW QUESTION # 99
Discovering and documenting metadata about physical data assets provides:
Answer: C
Explanation:
Discovering and documenting metadata about physical data assets provides visibility into how data moves and is transformed between systems. This is a core function of technical metadata and data lineage.
DAMA-DMBOK2 states that technical metadata describes the technical characteristics of data, the systems that store it, and the processes that move it within and between systems. Examples include physical table and column names, ETL job information, source-to-target mappings, and lineage documentation with upstream and downstream impact information. The certification question therefore points directly to option D; the same interpretation is independently reflected in published CDMP-oriented material.
This capability is critical to Data Quality because a defect observed in a report or downstream application may not originate in the current system. It may have been introduced through extraction logic, transformation rules, reference-data conversion, aggregation, or loading.
Documented physical metadata allows analysts to trace the affected value backward, identify its source, inspect each transformation, and isolate the point at which the defect occurred. It also supports change-impact analysis: modifying a source field or mapping can reveal which downstream assets may be affected.
Metadata therefore provides the evidence required for systematic root-cause analysis rather than symptom- based correction.
Reference Topics: DAMA-DMBOK2 Metadata Management - Technical Metadata; Source-to-Target Mapping; Data Lineage; Data Integration and Interoperability; Chapter 13 - Root-Cause Analysis and Quality Monitoring.
NEW QUESTION # 100
An MDM hub receives three customer records that have been confirmed to represent the same person. Before creating the mastered representation, what process determines which source values should populate each attribute?
Answer: C
Explanation:
The process is Survivorship. After records have been matched and determined to represent the same entity, survivorship rules determine which attribute values should populate the mastered or preferred representation.
For example, the organization may select the most recently verified telephone number, prioritize a government-verified legal name, or prefer addresses originating from a designated Customer Relationship Management system. Different attributes can use different survivorship rules.
Survivorship is not simply "choose the newest value." The appropriate method may consider source trust, verification status, recency, completeness, confidence scores, stewardship decisions, or combinations of these factors.
The rules must be governed because they determine what the organization subsequently treats as trusted master data. Metadata should record the rule, selected source, lineage, and effective date so the mastered record remains explainable.
DAMA identifies Reference and Master Data Management as responsible for maintaining consistency in core entities such as customers, products, and locations and for supporting trusted shared information.
The Data Quality dimensions involved commonly include Accuracy, Consistency, Currency, Completeness, and Uniqueness.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Golden Record; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 101
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