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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Topic 2: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Topic 3: Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Topic 4: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 5: Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
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NEW QUESTION # 73
Which of the following activities is NOT a way that enterprise data architecture influences the scope boundaries of projects?
Answer: B
Explanation:
Ensuring enterprise business processes are effectively documented is not primarily a Data Architecture mechanism for defining project scope boundaries. That responsibility belongs more directly to Business Architecture, process management, and business-analysis disciplines.
DAMA-DMBOK2 explains that Enterprise Data Architecture influences projects by defining enterprise data requirements, reviewing project data designs, determining lineage impacts, controlling unnecessary replication, enforcing Data Architecture standards, and guiding technology and renewal decisions.
Consequently, options A, B, D, and E all represent valid ways architecture can constrain or guide project scope. A design review checks that local solutions remain compatible with enterprise strategy. Standards prevent individual projects from creating incompatible structures. Replication controls limit uncontrolled proliferation of redundant data. Enterprise data requirements ensure that projects account for information needs beyond their immediate application boundaries.
Documenting business processes remains important because processes produce and consume data, but comprehensive process documentation is not itself a primary Enterprise Data Architecture scope-control activity.
From a Data Quality perspective, architectural influence prevents project-level decisions from creating duplicated authoritative sources, inconsistent definitions, undocumented lineage, or uncontrolled transformations.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Manage Enterprise Requirements within Projects; Architecture Governance; Project Scope Boundaries; Data Replication; Chapter 13 - Consistency and Lineage.
NEW QUESTION # 74
Data modelling tools and model repositories are necessary for:
Answer: D
Explanation:
DAMA-DMBOK2 states that Data Modeling tools and model repositories are necessary for managing the Enterprise Data Model at all levels. This includes maintaining relationships among conceptual, logical, and physical representations and controlling how models created for different purposes fit into the wider enterprise architecture.
An enterprise modeling repository provides more than diagramming. It supports version control, definitions, lineage between model layers, comparison of changes, reuse of standard entities and attributes, enforcement of modelling conventions, and communication across projects. Many modelling tools also provide relationship and lineage capabilities that enable architects to trace how concepts represented at a high level are implemented in detailed models.
Option C describes one important capability-visualizing and communicating database designs-but it is narrower than the DMBOK2 purpose stated in the question. Similarly, modelling tools support governance and architectural visualization but are not solely established for either function.
For Data Quality, governed models provide the structural metadata from which rules can be derived. Keys support uniqueness; relationships support integrity; domains support validity; mandatory attributes support completeness; and standardized definitions support consistency.
The repository therefore becomes an important bridge between Data Architecture, Metadata Management, Data Governance, and Data Quality.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling Tools and Repositories; Enterprise Data Model; Conceptual/Logical/Physical Models; Chapter 13 - Structural Quality Rules.
NEW QUESTION # 75
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: A
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 # 76
The goal of data governance is to enable an organisation to manage data as an asset. To achieve this, the DG programs must be:
Answer: D
Explanation:
DAMA-DMBOK2 explicitly states that a Data Governance program must be sustainable. Governance is not a temporary implementation project that ends after policies, committees, or stewardship roles are established. It is an ongoing organizational capability requiring continuing leadership, sponsorship, ownership, decision rights, and operational integration.
The DMBOK2 governance guidance describes sustainable governance as "sticky": it must survive beyond its initial implementation and become embedded in normal business and Data Management practices.
Sustainable governance depends specifically on business leadership, sponsorship, and ownership.
This distinction matters for Data Quality because quality improvement is likewise continuous. New applications, data sources, business processes, regulatory requirements, and transformations continually create new risks. Governance must therefore continue assigning accountability, approving definitions and quality rules, resolving cross-domain disputes, and overseeing remediation.
Options focused on financial registration or assigning a dollar value confuse data-as-an-asset thinking with formal accounting treatment. Data valuation can support investment decisions, but it is not a prerequisite for Data Governance. Likewise, treating governance as a fixed-duration initiative contradicts the operating model described by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Goals and Principles; Sustainable Governance; Leadership; Sponsorship; Ownership; Chapter 13 - Data Quality Governance.
NEW QUESTION # 77
A security mechanism that searches for customer bank account details in outgoing emails is achieving the goal of:
Answer: E
Explanation:
The mechanism is designed to meet confidentiality and privacy requirements. Customer bank-account details constitute sensitive financial information. Inspecting outgoing email for such values is a form of Data Loss Prevention control intended to identify or prevent inappropriate disclosure before sensitive information leaves the organization's controlled environment.
DAMA-DMBOK2 treats confidentiality and privacy as fundamental Data Security requirements. Its electronic-communication guidance warns that restricted or confidential information should not be sent through insecure communication channels because messages can be intercepted, forwarded, or otherwise disclosed after leaving the sender's control. DAMA International's own privacy practices likewise classify bank-account and similar financial information as protected personal/financial data requiring security safeguards against unauthorized disclosure.
The other responses concern transparency, definitions, user experience, or availability and do not address the security objective demonstrated in the scenario.
Data Governance determines the classifications and policies governing such information; Metadata Management can record sensitivity classifications against physical data elements; and security technology then enforces the resulting controls.
Data Quality remains relevant because confidentiality controls must distinguish genuinely sensitive values accurately. Poor classification or inaccurate detection rules may either expose protected information or unnecessarily block legitimate communication.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Confidentiality and Privacy; Electronic Communication Security; Sensitive Data; Data Loss Prevention; Data Governance.
NEW QUESTION # 78
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