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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Integration with data governance - Roles and responsibilities |
| Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Data profiling techniques - Defining quality rules and metrics |
| 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% | - Data quality dimensions - Definition and importance of data quality - Data quality in DMBOK framework |
| Data Quality Improvement | 20% | - Monitoring and continuous improvement - Data cleansing and standardization - Root cause analysis |
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NEW QUESTION # 107
A goal of reference and master data management is for data to ensure shared data is:
Answer: E
Explanation:
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
NEW QUESTION # 108
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 # 109
A minimal super key is:
Answer: C
Explanation:
A candidate key is a minimal super key. A super key is any set of attributes sufficient to uniquely identify an entity instance, but it may contain attributes that are unnecessary for uniqueness. A candidate key removes that redundancy: if any attribute is removed from the candidate key, the remaining attributes no longer uniquely identify the entity.
DAMA-DMBOK2 makes this distinction explicitly: a candidate key is a minimal set of one or more attributes identifying an entity instance, and "minimal" means that no subset of the candidate key can perform the same unique-identification function.
Option B is incomplete because a set can uniquely identify an entity while still containing unnecessary attributes; that would qualify as a super key but not necessarily a minimal super key. A surrogate key is different: it is an artificial identifier introduced primarily for technical identification. Foreign-key composition and physical index structures likewise do not define candidate-key minimality.
This concept has direct Data Quality implications. Correctly defined candidate and primary keys support uniqueness and integrity, prevent duplicate entity instances, enable reliable referential relationships, and improve entity matching within Master Data Management. Key definitions should also be captured as structural metadata so profiling and quality rules can consistently test duplicate and orphan conditions.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Keys; Candidate Keys; Chapter 13 - Uniqueness and Integrity; Metadata Management; Master Data Management.
NEW QUESTION # 110
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 # 111
Discovering and documenting metadata about physical data assets provides:
Answer: E
Explanation:
Discovering and documenting metadata about physical data assets provides visibility into how data moves and is transformed between systems. This is a core function of technical metadata and data lineage.
DAMA-DMBOK2 states that technical metadata describes the technical characteristics of data, the systems that store it, and the processes that move it within and between systems. Examples include physical table and column names, ETL job information, source-to-target mappings, and lineage documentation with upstream and downstream impact information. The certification question therefore points directly to option D; the same interpretation is independently reflected in published CDMP-oriented material.
This capability is critical to Data Quality because a defect observed in a report or downstream application may not originate in the current system. It may have been introduced through extraction logic, transformation rules, reference-data conversion, aggregation, or loading.
Documented physical metadata allows analysts to trace the affected value backward, identify its source, inspect each transformation, and isolate the point at which the defect occurred. It also supports change-impact analysis: modifying a source field or mapping can reveal which downstream assets may be affected.
Metadata therefore provides the evidence required for systematic root-cause analysis rather than symptom- based correction.
Reference Topics: DAMA-DMBOK2 Metadata Management - Technical Metadata; Source-to-Target Mapping; Data Lineage; Data Integration and Interoperability; Chapter 13 - Root-Cause Analysis and Quality Monitoring.
NEW QUESTION # 112
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