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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Topic 2: Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
| Topic 3: Data Quality Concepts and Principles | 15% | - Data quality dimensions - Data quality in DMBOK framework - Definition and importance of data quality |
| Topic 4: Data Quality Strategy and Governance | 20% | - Integration with data governance - Data quality strategy and policy - Roles and responsibilities |
| Topic 5: Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Data quality in lifecycle management - Business value and ROI |
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NEW QUESTION # 80
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: D
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 # 81
The requirement to enter a username, a password and then a code sent to an authentication app is called:
Answer: A
Explanation:
This is two-factor authentication (2FA). Authentication factors are distinguished by the type of evidence used to verify identity. A username identifies the account but does not constitute a separate authentication factor.
The password represents something the user knows, while the code generated or delivered through an authentication application represents something the user has. Because two different factor categories are involved, the mechanism is two-factor authentication.
DAMA-DMBOK2 discusses multiple-factor identification as a security control for systems containing sensitive information. Its examples include a code returned to a user's mobile device, possession of a hardware device, and biometric factors such as fingerprints or facial recognition. DMBOK2 specifically notes that two-factor identification makes unauthorized account access substantially more difficult.
Three-factor authentication would require a third independent category, typically something the user is, such as a biometric characteristic. The mere fact that a username, password, and code are entered does not create three factors because the username is an identifier rather than authentication evidence.
From a Data Quality perspective, strong authentication protects integrity by reducing the risk that unauthorized users alter governed data, quality rules, metadata, or master records.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authentication; Multiple-Factor Identification; Authorization; Data Security; Data Integrity.
NEW QUESTION # 82
When a data quality team has more issues than they can manage, they should look to:
Answer: A
Explanation:
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
NEW QUESTION # 83
The goals of data storage and operations are:
Answer: E
Explanation:
DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.
Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.
Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.
Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.
Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.
The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 - Data Reliability and Timeliness.
NEW QUESTION # 84
What is one of the Data Architecture artifacts which are usually captured within development projects, and then standardised and managed by data architects?
Answer: D
Explanation:
The correct answer is Data models. DAMA-DMBOK2 explicitly explains that data models and other Data Architecture artifacts are commonly created or captured within development projects and are subsequently standardized and managed by Data Architects.
Development projects routinely produce conceptual, logical, and physical representations of the data required by a solution. If each project develops those structures independently without architectural review, inconsistent terminology, duplicated entities, conflicting definitions, and incompatible relationship structures can accumulate across the enterprise. Data Architects therefore review project models, reconcile them with enterprise standards, and identify structures suitable for reuse.
An Enterprise Data Model is itself an important architectural artifact, but the wording of the question refers specifically to artifacts typically generated in individual development projects and later standardized. The DMBOK2 passage uses data models in precisely that context.
This process has a direct Data Quality benefit. Standardized models improve consistency of definitions, domains, keys, relationships, and constraints. They also provide metadata from which integrity, uniqueness, validity, and completeness rules can be derived.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Architectural Artifacts; Development Projects; Chapter 5 - Data Modeling and Design; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 85
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