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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
| Topic 2: Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Topic 3: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Topic 4: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 5: Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
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NEW QUESTION # 54
Which of the following is a directive that codifies principles and management intent into fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information?
Answer: B
Explanation:
The definition describes Data Policies. DAMA-DMBOK2 states that data policies are directives that translate organizational principles and management intent into fundamental rules controlling how data is created, acquired, protected, maintained, assured for quality, and used.
The distinction between governance and policy is important. Data Governance is the broader authority and decision-making framework. Policies are one of the principal instruments produced or sponsored through that framework. They define what must or must not occur. Standards and procedures subsequently translate those policies into specific implementation requirements and operational methods.
For Data Quality, a policy might require business-critical data to have designated ownership, documented definitions, measurable quality rules, monitored thresholds, and formal remediation processes. The detailed thresholds themselves may reside in standards or rule repositories rather than in the high-level policy.
Metadata Management makes policy operational by documenting definitions, classifications, ownership, lineage and applicable quality rules. Master Data Management applies those policies to shared business entities and controlled reference values. Data Stewards then monitor compliance and escalate violations through governance mechanisms.
Data asset valuation is concerned with determining economic value, while Data Management encompasses the complete discipline. Neither provides the specific "directive" definition stated in the question.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Policies; Governance Principles; Standards and Procedures; Chapter 13 - Data Quality Governance; Metadata and Master Data interactions.
NEW QUESTION # 55
How is Data Governance defined?
Answer: D
Explanation:
DAMA-DMBOK2 defines Data Governance as the exercise of authority and control over the management of data assets. This definition distinguishes governance from the operational execution of Data Management.
Independent literature describing DAMA's framework uses the same core formulation, and the DAMA Wheel places Data Governance across the other knowledge areas through this authority-and-control function.
Governance establishes decision rights, accountability, policies, standards, stewardship, issue escalation, compliance expectations, and mechanisms for monitoring whether data is being managed appropriately. It determines who is authorized to make decisions and what rules must be followed; individual Data Management knowledge areas then execute the corresponding operational activities.
Option E is closer to the broad definition of Data Management, because it refers to planning, implementation, and control across the information lifecycle. Option A describes an assessment or maturity activity. Option B describes selected governance benefits rather than its definition, while option C characterizes a collection of functions.
For Data Quality, governance provides the authority needed to define critical data, approve quality requirements and thresholds, assign accountability, prioritize remediation, and enforce standards. Without this authority structure, profiling may identify defects but the organization may lack the decision mechanisms required to correct their underlying causes.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Definition; Authority and Control; Policies and Standards; Chapter 13 - Data Quality Governance.
NEW QUESTION # 56
Three common interaction models for data integration are:
Answer: D
Explanation:
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
NEW QUESTION # 57
What are the types of models that are modelled in data models?
Answer: A
Explanation:
DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.
Category information classifies things-for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.
Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.
This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.
Data models also generate important metadata-definitions, relationships, domains and constraints-which becomes the basis for measurable Data Quality rules.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Types of Data that are Modeled; Chapter 10 - Reference and Master Data; Chapter 13 - Data Quality Rules and Dimensions; Metadata Management.
NEW QUESTION # 58
In Data Modelling, the generalization of the concept of person and organisation into a party enables:
Answer: B
Explanation:
The Party pattern generalizes Person and Organization under a common supertype so that both can participate consistently in shared business relationships and processes. The major benefit is that persons and organizations can act in roles in agreements, share address structures, and participate in common interaction processes, making option E correct.
For example, a Customer, Supplier, Account Holder, Employee, or Contracting Party may be either a person or an organization. Without the Party abstraction, separate relationships may need to be constructed repeatedly for Person and Organization. With Party, common concepts such as Agreement, Address, Contact Point, Role, and Interaction can reference a single generalized entity.
This is particularly relevant to Master Data Management because Party is a common enterprise modelling pattern for Customer, Supplier, and related master-data domains. It supports entity resolution while preserving distinctions between individuals and organizations through subtype structures.
From a Data Quality perspective, the approach improves consistency by reducing duplicated relationship definitions and business rules. However, governance must still establish subtype-specific requirements-for example, organization registration identifiers versus personal names and dates of birth.
The abstraction is therefore primarily about reusable semantic relationships and roles, not security concealment or faster package implementation.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Generalization and Specialization; Party Model; Chapter 10 - Master Data; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 59
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