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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Data Quality Governance | 15% | - Continuous Monitoring - Data Quality Roles and Responsibilities - Policies and Procedures - Data Stewardship |
| Data Quality Assessment and Measurement | 25% | - Metrics and KPIs - Profiling Techniques - Root Cause Analysis - Benchmarking |
| Data Quality Improvement | 20% | - Validation Rules - Standardization - Data Enrichment - Data Cleansing Techniques |
| Data Quality Frameworks and Standards | 20% | - Other Industry Standards - ISO 8000 (Data Quality) - DAMA DMBOK Framework |
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NEW QUESTION # 43
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
Answer: A
Explanation:
A complete absence of reported issues immediately after introducing an enterprise Data Issue process is more likely to indicate lack of confidence in the governance process than genuinely flawless enterprise data. The certification material explicitly identifies lack of credibility in the process's ability to effect change as the plausible explanation.
Effective issue management depends on organizational trust. Employees need to believe that documenting an issue will lead to triage, ownership, escalation, root-cause investigation, remediation, and appropriate communication. If previous problems disappeared into a queue without action-or if raising defects creates organizational friction-users may simply stop reporting them.
DAMA-DMBOK2 treats Data Governance implementation as an organizational-change challenge rather than a purely procedural exercise. Governance must demonstrate authority, responsiveness, transparency, and measurable outcomes to establish credibility.
For Data Quality, issue volumes must also be interpreted carefully. "Zero issues reported" is not equivalent to
"zero defects." Complementary evidence should come from profiling, automated monitoring, quality metrics, user feedback, reconciliation, and operational outcomes.
Management should investigate reporting barriers, communicate resolved cases, establish clear escalation paths, and demonstrate that identified problems produce tangible improvements.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Adoption and Credibility; Data Issue Management; Chapter 13 - Issue Identification, Escalation, Root-Cause Analysis and Remediation.
NEW QUESTION # 44
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
Answer: C
Explanation:
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
NEW QUESTION # 45
A control chart shows a stable defect rate for several weeks, followed by one point far outside the established control limits. This most strongly indicates:
Answer: D
Explanation:
A point materially outside established control limits is a classic signal of special-cause variation. Statistical Process Control distinguishes routine variation inherent in a stable process from unusual variation introduced by an identifiable event or change.
When a Data Quality process normally produces a stable percentage of invalid records and a sudden observation lies beyond the expected statistical range, investigation should focus on what changed. Possible causes include a new source system, software deployment, changed mapping, revised business rule, unusual batch, or manual processing error.
The current DMBOK2 Chapter 13 revision retains Statistical Process Control concepts while reorganizing them into supporting material and explicitly maps Shewhart/Deming improvement stages to Data Quality processes.
The signal does not prove what caused the defect, nor does it establish that performance has improved or deteriorated permanently. It indicates that the observed variation is sufficiently unusual to justify analysis.
Quality teams should preserve the evidence, correlate the timing with system and process changes, use lineage to identify affected sources, and investigate before adjusting the process unnecessarily.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Control Charts; Common Cause; Special Cause; Continuous Improvement.
NEW QUESTION # 46
Database monitoring tools measure key database metrics, such as:
Answer: C
Explanation:
DAMA-DMBOK2 identifies capacity, availability, cache performance, and user statistics as representative metrics captured by database-monitoring tools. Database monitoring is an operational control mechanism used by Database Administrators and platform teams to understand whether database infrastructure is available, adequately sized, responsive, and being used as expected. DMBOK2 explicitly describes monitoring tools as automating the observation of metrics such as capacity, availability, cache performance, and user statistics.
Capacity metrics indicate resource consumption and future growth requirements. Availability measures whether data services remain accessible when required. Cache-performance measures help identify inefficient access patterns and bottlenecks, while user statistics provide information about workload and database consumption.
The other choices mix design concepts, CRUD operations, or Data Quality concepts with operational monitoring measures. For example, normalization is a modelling technique rather than a routine runtime performance metric. Similarly, "create, read, update, delete" describes basic data operations rather than monitoring indicators.
Although database performance and Data Quality are distinct disciplines, poor operational performance can affect Data Quality dimensions such as timeliness and availability. Monitoring therefore supports the technical environment in which governed, reliable information is delivered to users and applications.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Database Operations; Capacity and Availability Management; Monitoring; Chapter 13 - Timeliness and Operational Fitness for Purpose.
NEW QUESTION # 47
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: C
Explanation:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
NEW QUESTION # 48
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