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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality - Data Quality in the Data Lifecycle |
| Topic 2: Data Quality Improvement | 20% | - Data Cleansing Techniques - Validation Rules - Standardization - Data Enrichment |
| Topic 3: Data Quality Assessment and Measurement | 25% | - Profiling Techniques - Metrics and KPIs - Root Cause Analysis - Benchmarking |
| Topic 4: Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - Other Industry Standards - DAMA DMBOK Framework |
| Topic 5: Data Quality Governance | 15% | - Continuous Monitoring - Data Quality Roles and Responsibilities - Data Stewardship - Policies and Procedures |
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NEW QUESTION # 51
The implementation of data architecture exposes the transformation of data as it moves across the landscape.
A common name for this concept is:
Answer: D
Explanation:
The concept described is Data Lineage. Data lineage records how data originates, moves, transforms, and is consumed across the information landscape. DAMA-oriented architecture guidance explicitly links implementation of Data Architecture with visibility into transformations occurring as data traverses systems and identifies this as lineage.
Lineage can operate at several levels. At a high level, it may identify that customer information moves from CRM into an integration platform, Master Data hub, warehouse, and reporting environment. At a detailed level, it may show that a particular report column derives from a specific source attribute through documented calculations, mappings, and transformation rules.
This capability is fundamental to Data Quality. When an incorrect result appears in a report, lineage helps analysts trace the defect upstream to the point where it originated or was introduced. It also supports root- cause analysis, impact assessment, regulatory traceability, change management, and reconciliation.
Metadata Management provides the repository structures needed to capture and maintain lineage. Data Governance determines which lineage must be documented and who is accountable for maintaining it.
ETL is one mechanism through which data may move and transform, but ETL is not the architectural concept describing the end-to-end history and derivation of the data.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Chapter 11 - Metadata Management; Data Lineage; Chapter 13 - Root-Cause Analysis and Data Quality Traceability.
NEW QUESTION # 52
A Data Quality score is calculated from Completeness, Accuracy, and Timeliness. Why might the dimensions be weighted differently?
Answer: D
Explanation:
Dimensions may be weighted differently because their business impact is not necessarily equal for a particular dataset or process.
For an emergency-response system, Timeliness may be more critical than completeness of optional descriptive fields. For regulatory reporting, Accuracy and Integrity may carry substantially greater risk than minor delays. For a marketing contact list, completeness and currency may dominate.
A composite Data Quality score should therefore reflect business priorities rather than assuming that each dimension contributes identical value. Weighting must be transparent and governed; otherwise a high score in low-impact dimensions could hide serious failure in a critical one.
The first step remains defining business requirements and Critical Data Elements. Once those are established, dimensions, thresholds, and weights can be linked to documented business consequences.
A score should also retain underlying component metrics. A single aggregate percentage is insufficient for diagnosis because two datasets with the same overall score may have very different risk profiles.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Metrics; Scorecards; Business Impact; Critical Data Elements; Quality Dimensions; Thresholds.
NEW QUESTION # 53
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 # 54
Big data management requires:
Answer: E
Explanation:
DAMA-DMBOK2 establishes the guiding principle that Big Data management requires more discipline than relational data management. The reason is not simply data volume. Big Data environments combine large volumes with substantial variation in structure, source, velocity, semantics, and reliability. These characteristics increase the probability of uncontrolled duplication, inconsistent interpretation, poor provenance, inappropriate use, and undetected quality defects. DAMA's Big Data guidance specifically emphasizes disciplined management of metadata describing Big Data sources, their origins, and their value.
From a Data Quality perspective, scale does not make data fit for purpose. Large datasets still require defined quality expectations, profiling, monitoring, lineage, controlled transformations, ownership, and governance.
Metadata becomes particularly important because analysts must understand where datasets originated, how they were transformed, their meaning, and whether they are appropriate for a particular analytical objective.
Data Governance must consequently establish accountability, acceptable-use policies, security requirements, quality expectations, and escalation mechanisms. Statistical and automated controls are also essential because manual inspection becomes impractical at Big Data scale.
Neither larger budgets nor data-science certification substitutes for sound data-management discipline.
Likewise, reducing controls because data is large creates greater-not lower-operational and analytical risk.
Reference Topics: DAMA-DMBOK2 - Big Data and Data Science; Big Data Management Principles; Metadata Management; Data Governance; Chapter 13 - Fitness for Purpose and Data Quality Controls.
NEW QUESTION # 55
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 # 56
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