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| Section | Objectives |
|---|---|
| Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
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NEW QUESTION # 22
The Family Name of a Person is recorded in a system. The column name is Pname. Pname is an example of:
Answer: A
Explanation:
Pname is Metadata because it is the name assigned to a database column and therefore describes the structure in which the actual Family Name values are stored. A value such as Smith or Patel would be data; Pname is structural information describing that data.
DAMA-DMBOK2 defines Metadata broadly as information about data and data-related processes. Technical metadata includes physical database characteristics such as table names, column names, datatypes, lengths, indexes, constraints, and related structural properties. The interpretation of Pname as metadata is also consistent with the published version of this exact certification question.
The fact that Pname may be an unclear abbreviation does not change its classification. It may represent a poor naming convention from a usability or governance perspective, but technically it remains metadata. A stronger physical name might be FamilyName, while the metadata repository could additionally map that field to the authoritative business glossary term.
This distinction matters to Data Quality because rules are frequently attached to metadata objects. For example, the Family Name attribute might have completeness, permitted-character, maximum-length, and standardization requirements.
Effective Metadata Management connects the technical column to its business meaning, system lineage, stewardship, and applicable Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 11 - Technical Metadata; Database Metadata; Business Glossary; Chapter 13 - Data Quality Rules and Metadata.
NEW QUESTION # 23
An Orders table contains an order whose Customer_ID does not exist in the Customer table. Which Data Quality dimension is most directly violated?
Answer: C
Explanation:
The primary issue is Data Integrity, specifically referential integrity. The Orders record references a Customer_ID that does not correspond to an existing Customer record, meaning the relationship defined by the data model has been violated.
Integrity concerns whether structural relationships and constraints among data elements remain valid. In relational environments, this commonly includes primary-key uniqueness, foreign-key relationships, mandatory relationships, and cardinality constraints.
A customer identifier could be syntactically valid and populated, yet still fail integrity because no corresponding parent entity exists. This illustrates why integrity is different from completeness or validity.
The DMBOK2 dimension model explicitly associates integrity with unique identifiers, cardinality, and referential integrity concepts.
Remediation should determine why the orphan record occurred. Potential causes include incorrect load sequencing, deletion of the parent record, integration failure, transformation defects, or absence of database constraints.
Metadata and Data Modeling establish the expected relationship; Data Quality controls then measure whether operational data conforms to it.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Integrity; Referential Integrity; Chapter 5 - Relationships and Keys; Data Integration Controls.
NEW QUESTION # 24
The implementation of a 'Super Type - Sub Type' structure can use the following 2 options:
Answer: B
Explanation:
DAMA-DMBOK2 identifies two recognized approaches for resolving logical supertype-subtype abstractions when moving into physical database design: Subtype Absorption and Supertype Partition.
With Subtype Absorption, attributes belonging to subtype entities are incorporated into the table representing the supertype. Attributes that apply only to particular subtypes may consequently be nullable. This approach reduces the number of physical tables but can introduce sparsity and requires explicit rules to ensure subtype- specific attributes remain semantically valid.
With Supertype Partition, the attributes belonging to the supertype are carried into separate physical tables representing each subtype. This can simplify subtype-specific processing but introduces duplication of common structures and requires disciplined metadata and modelling control.
The choice has direct consequences for Data Quality. Subtype absorption requires validity and completeness rules to distinguish legitimate nulls from missing data. Supertype partition requires consistency controls to ensure shared attributes retain identical definitions and constraints across subtype tables. Metadata repositories should document subtype discriminators, attribute definitions, constraints, and inheritance rules.
DMBOK2 therefore treats the transformation as a deliberate physical modelling decision rather than the generic "merge/split" terminology presented in the distractors.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Physical Data Modeling; Resolve Logical Abstractions; Chapter 13 - Validity, Completeness and Consistency; Metadata Management.
NEW QUESTION # 25
Data modelling tools and model repositories are necessary for:
Answer: C
Explanation:
DAMA-DMBOK2 states that Data Modeling tools and model repositories are necessary for managing the Enterprise Data Model at all levels. This includes maintaining relationships among conceptual, logical, and physical representations and controlling how models created for different purposes fit into the wider enterprise architecture.
An enterprise modeling repository provides more than diagramming. It supports version control, definitions, lineage between model layers, comparison of changes, reuse of standard entities and attributes, enforcement of modelling conventions, and communication across projects. Many modelling tools also provide relationship and lineage capabilities that enable architects to trace how concepts represented at a high level are implemented in detailed models.
Option C describes one important capability-visualizing and communicating database designs-but it is narrower than the DMBOK2 purpose stated in the question. Similarly, modelling tools support governance and architectural visualization but are not solely established for either function.
For Data Quality, governed models provide the structural metadata from which rules can be derived. Keys support uniqueness; relationships support integrity; domains support validity; mandatory attributes support completeness; and standardized definitions support consistency.
The repository therefore becomes an important bridge between Data Architecture, Metadata Management, Data Governance, and Data Quality.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling Tools and Repositories; Enterprise Data Model; Conceptual/Logical/Physical Models; Chapter 13 - Structural Quality Rules.
NEW QUESTION # 26
A company has five systems that can update a customer's legal name, and no system is designated as authoritative. What is the greatest Data Quality risk?
Answer: C
Explanation:
The principal risk is inconsistent customer-name values across systems. When multiple applications can independently update the same business fact without an authoritative source or coordinated Master Data process, conflicting representations are likely to emerge.
The problem is architectural and governance-related as much as it is a Data Quality issue. The organization must determine where the authoritative value is created, who is responsible for approving changes, how updates propagate, and how conflicts are resolved.
DAMA's public framework emphasizes a single trusted or consistent approach to shared core entities through Reference and Master Data Management.
The relevant Data Quality dimensions include Consistency, Accuracy, Currency, and potentially Uniqueness.
An application may hold a technically valid legal name but still contain an outdated or conflicting value.
Metadata and lineage should document which systems create, master, distribute, or merely consume the attribute.
Reference Topics: DAMA-DMBOK2 - Authoritative Sources; Master Data Management; Data Architecture; Consistency; Lineage; Stewardship.
NEW QUESTION # 27
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