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| Section | Objectives |
|---|---|
| Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
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NEW QUESTION # 43
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: B
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 # 44
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 # 45
The Data Architecture workstream on results produces:
Answer: E
Explanation:
Within DAMA-DMBOK2, establishing a Data Architecture practice is divided into several complementary workstreams. The Results workstream is specifically responsible for producing Data Architecture artifacts within an overall roadmap. The other answer choices correspond to different aspects of the architecture practice rather than to the Results workstream itself.
DMBOK2 distinguishes the workstreams clearly: Strategy selects frameworks and develops the roadmap; Acceptance and Culture addresses awareness and behavioural change; Organization assigns architecture accountability and responsibility; Working Methods establish practices and coordinate architecture work with development initiatives; and Results produces the architectural artifacts that collectively realize the roadmap.
Typical artifacts include enterprise data models, high-level data-flow designs, architectural standards, transition-state specifications, and related metadata. These artifacts represent both current and target architectural states and allow individual projects to align with enterprise direction.
The Data Quality connection is substantial because architectural artifacts identify authoritative sources, integration points, critical data flows, entities, relationships, and dependencies. Those structures provide the context needed to establish validity, integrity, consistency, lineage, and quality-monitoring requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Establish Data Architecture Practice; Results Workstream; Data Architecture Artifacts; Architecture Roadmap; Chapter 13 - Data Quality Dependencies.
NEW QUESTION # 46
Before defining a Data Quality metric for a business-critical field, the team should first:
Answer: A
Explanation:
A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.
For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.
The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.
DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.
Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.
Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.
NEW QUESTION # 47
HTTPS:// indicates that the website is:
Answer: C
Explanation:
An address beginning with HTTPS indicates that the website uses an encrypted security layer for communications between the client and server. DAMA-DMBOK2 explicitly identifies HTTPS as a security technology and states that the https:// prefix indicates that a website is equipped with an encrypted security layer.
HTTPS uses TLS to protect information while it is transmitted across the network. Its principal security objectives include confidentiality of the communication channel, integrity of transmitted information, and authentication of the server through digital certificates. This is especially important when transmitting credentials, personal information, payment details, or other sensitive data.
HTTPS does not indicate what database technology the website uses, whether a content management system is installed, whether third-party cookies are present, or whether language-translation functionality exists.
Those characteristics are independent of transport-layer encryption.
From a Data Management standpoint, encryption in transit is one component of a broader Data Security framework. Sensitive data must also be protected at rest, appropriately classified, access-controlled, monitored, and governed throughout its lifecycle.
Data Quality also benefits indirectly because integrity protection helps prevent unauthorized alteration of values during transmission.
Reference Topics: DAMA-DMBOK2 Chapter 7 - HTTPS; Encryption; Data in Transit; Authentication; Confidentiality; Integrity.
NEW QUESTION # 48
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