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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Integration with data governance - Data quality strategy and policy |
| Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
| 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 - Defining quality rules and metrics - Data profiling techniques |
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NEW QUESTION # 33
Who does the DMBoK consider to be generally responsible for developing business glossary content?
Answer: A
Explanation:
DAMA-DMBOK2 assigns primary responsibility for business glossary content to Business Data Stewards.
Business Data Stewards are typically subject-matter experts who understand how data is defined, created, interpreted, and consumed within their business domain. DMBOK2 explicitly states that Data Stewards are generally responsible for business glossary content and identifies Business Data Stewards as professionals who work with stakeholders to define and control data.
A business glossary is not simply a technical dictionary. It establishes agreed business terminology, definitions, synonyms, business rules, responsible stewards, and relationships between business concepts.
This makes stewardship involvement essential because definitions must reflect operational and business meaning rather than merely database structures.
Data Architects may contribute candidate definitions and structural context from subject-area and conceptual models, but they do not generally own the business meaning. Business users provide valuable input, while Coordinating Data Stewards help reconcile definitions across domains, yet the normal accountability remains with Business Data Stewards.
This relationship is particularly important for Data Quality. Quality rules depend on unambiguous definitions of data elements. If "Customer," "Active Account," or "Order Date" has inconsistent meanings, measurements of completeness, accuracy, and validity cannot be consistently interpreted.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Develop a Business Glossary; Business Data Stewardship; Metadata Management; Chapter 13 - Data Quality Rules and Business Definitions.
NEW QUESTION # 34
In 2009, ARMA International published GARP for managing records and information. GARP stands for:
Answer: B
Explanation:
GARP stands for Generally Accepted Recordkeeping Principles. ARMA International developed the framework to describe the characteristics of an effective records and information-management program. The principles address accountability, transparency, integrity, protection, compliance, availability, retention, and disposition.
These principles align closely with DAMA-DMBOK2's treatment of Document and Content Management.
Information must be created, organized, protected, maintained, retrieved, retained, and ultimately disposed of according to business, legal, regulatory, and historical requirements. Records management is therefore not simply about long-term storage. It establishes controls throughout the information lifecycle.
For example, the Integrity principle addresses the authenticity and reliability of records. Availability requires information to be retrievable accurately and efficiently. Retention requires organizations to keep records for justified periods, while Disposition governs appropriate final handling after those obligations expire.
These controls interact directly with Data Quality. Records that cannot be found, trusted, interpreted, or demonstrated to be authentic are not fit for their intended purpose even if they physically exist.
Metadata Management supplies classification, retention, provenance, ownership, and lifecycle metadata necessary to implement these principles consistently.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Records Management; GARP; Retention; Disposition; Integrity; Metadata Management.
NEW QUESTION # 35
The ethics of data handling, center on several core concepts. They are:
Answer: E
Explanation:
DAMA-DMBOK2 organizes Data Handling Ethics around three fundamental considerations: impact on people, potential for misuse, and the economic value of data. The text explains that data frequently represents individuals and is used in decisions that materially affect them; consequently, organizations have an ethical responsibility to ensure that data is reliable and appropriately managed. It also recognizes that data can be intentionally or unintentionally misused and that its economic value creates questions concerning ownership, access, rights, and permitted use.
These concepts directly intersect with Data Quality. Incorrect or misleading data may result in decisions that adversely affect customers, employees, patients, citizens, or other stakeholders. For that reason, accuracy, completeness, transparency, provenance, and appropriate interpretation are not merely technical characteristics-they can have ethical consequences.
Governance operationalizes these ethical principles through policies, decision rights, stewardship responsibilities, access rules, monitoring, and escalation procedures. Metadata Management supports transparency by documenting meaning, provenance, classification, and permitted use. Master Data Management also has an ethical dimension because consolidated person or customer records may create significant consequences when matching or identity-resolution errors occur.
Privacy and security are important controls, but they do not constitute DAMA's complete set of core ethical concepts.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Impact on People; Potential for Misuse; Economic Value of Data; Chapter 13 - Accuracy, Reliability and Governance.
NEW QUESTION # 36
An effective Data Governance communication program should include the following:
Answer: A
Explanation:
An effective Data Governance communication program should employ multiple complementary communication mechanisms, making All answers correct. Governance changes how people define, create, use, approve, and resolve issues with data; consequently, sustained adoption requires more than publishing policies.
Regular newsletters keep stakeholders aware of progress, decisions, metrics, and upcoming activities. A Data Governance Portal provides a persistent location for policies, standards, stewardship information, glossaries, issue processes, and supporting materials. Custom training develops the capabilities required for individuals to understand their specific governance responsibilities. Informal networking events help establish relationships across business and technical groups, which is particularly important when resolving data ownership and definition conflicts.
DAMA-DMBOK2 treats communication and organizational change as core implementation considerations because governance depends on participation across functions rather than on a single technical team.
Published CDMP material for this item identifies the combined response-newsletters, portal, training, and networking-as the intended answer.
For Data Quality, communication ensures that quality definitions, issue-management procedures, stewardship responsibilities, thresholds, and remediation decisions are understood and consistently applied.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Communications; Organizational Change; Training; Data Governance Portal; Stewardship Engagement; Chapter 13 - Data Quality Culture.
NEW QUESTION # 37
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: A
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 # 38
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