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| Section | Weight | Objectives |
|---|---|---|
| Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Data quality tools and technologies - Business value and ROI |
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
| Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
| Data Quality Strategy and Governance | 20% | - Integration with data governance - Data quality strategy and policy - Roles and responsibilities |
| Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Assessment methods and tools - Data profiling techniques |
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NEW QUESTION # 110
Profiling shows that whenever Account_Status is "Closed", Closure_Date is normally populated. However,
4% of closed accounts have a null Closure_Date. What type of profiling has revealed this issue?
Answer: D
Explanation:
This is cross-column dependency profiling. The issue cannot be identified merely by counting null values in Closure_Date because some nulls may be legitimate for active accounts. The defect becomes meaningful only when the relationship between two attributes is examined.
The implied business rule is: if Account_Status = Closed, then Closure_Date must be populated. Dependency profiling examines relationships among values and can reveal conditional completeness, inconsistent combinations, functional dependencies, and other multi-attribute patterns.
The next step is to confirm the inferred pattern with business stakeholders rather than automatically converting it into an enforced rule. There may be exceptional account categories where a closed status legitimately lacks a closure date.
Once approved, the rule should be documented as metadata, assigned to a Data Steward, measured against an agreed threshold, and monitored. Root-cause investigation can then determine whether missing dates arise from source-screen design, process bypass, conversion defects, or integration failures.
DAMA's revised Chapter 13 places Data Quality techniques in a more practical sequence and strengthens their linkage with Data Modeling and Metadata Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Dependency Analysis; Completeness; Business Rules; Metadata Management.
NEW QUESTION # 111
What is the final step in the development of a business-data-driven roadmap?
Answer: D
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 112
The main difference between a System of Record and a System of Reference is:
Answer: E
Explanation:
DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption.
A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.
Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.
This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.
Option D is incorrect because "System of Record" and "System of Reference" do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.
Reference Topics: DAMA-DMBOK2 Chapter 10 - System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 - Data Quality and Reference/Master Data Management.
NEW QUESTION # 113
Adoption of a Data Governance program is most likely to succeed:
Answer: A
Explanation:
DAMA-DMBOK2 recommends an incremental implementation strategy for Data Governance because governance introduces changes in accountability, decision rights, standards, processes, and organizational behaviour. Attempting an enterprise-wide implementation immediately creates unnecessary cultural and operational risk.
The implementation guidance is explicit that most governance rollout strategies are incremental, commonly beginning with a significant initiative such as Master Data Management or with a particular region, business unit, or domain. Enterprise-wide deployment as the initial step is described as uncommon.
Incremental implementation provides several control advantages. The organization can validate roles and decision mechanisms, establish stewardship practices, prove business value, refine policies, develop metrics, and correct weaknesses before expanding the governance framework. This aligns closely with Data Quality management, where organizations normally prioritize Critical Data Elements and high-value domains rather than attempting to measure and remediate every data element simultaneously.
Executive sponsorship is important, but a mandate alone does not produce sustainable adoption. Likewise, individual leadership charisma cannot substitute for operating processes, accountability, metrics, and change management. A short consultant-driven exercise also conflicts with DAMA's treatment of governance as an ongoing organizational capability.
Incremental rollout therefore offers the strongest mechanism for embedding governance permanently while demonstrating measurable value.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Implementation Guidelines; Organization and Culture; Adjustment and Communication; Chapter 13 - Data Quality Improvement and Governance.
NEW QUESTION # 114
A data lake and a data warehouse are the same concepts in so far as:
Answer: A
Explanation:
Although Data Lakes and Data Warehouses have materially different architectures, both support the broader objective of making organizational data available for reporting, analysis, analytics, and decision support.
Therefore, option B identifies their legitimate conceptual overlap.
A traditional Data Warehouse generally contains integrated, curated, structured data designed for repeatable Business Intelligence, reporting, and analytical workloads. A Data Lake typically retains larger volumes of raw or less-structured information and supports flexible processing, exploration, Data Science, and advanced analytics. Both can therefore participate in analytical data pipelines even though their approaches to schema, transformation, governance, and consumption differ.
They do not inherently duplicate every item of operational data. Nor must a Data Lake produce dimensional or star-schema models; that modeling approach is more closely associated with traditional warehouse implementations. Governance is required for both rather than being the defining difference between them.
From a Data Quality standpoint, warehouses typically apply substantial cleansing and conformity before consumption. Lakes may retain raw values and defer interpretation, which increases the importance of metadata, provenance, cataloging, profiling, and consumer awareness.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; Big Data; Data Lakes; Analytical Data; Metadata; Chapter 13 - Data Preparation and Fitness for Purpose.
NEW QUESTION # 115
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