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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Improvement | 20% | - Root cause analysis - Monitoring and continuous improvement - Data cleansing and standardization |
| Topic 2: Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
| Topic 3: Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Business value and ROI - Data quality tools and technologies |
| Topic 4: Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
| Topic 5: Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Data profiling techniques - Defining quality rules and metrics |
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NEW QUESTION # 49
A source application is modified so that a mandatory product identifier can no longer be left blank. Which type of Data Quality action is this?
Answer: D
Explanation:
The application change is a preventive Data Quality control because it prevents a known defect from being created in the first place.
A mandatory-field control ensures that records cannot be accepted without the required Product Identifier.
This differs from a detective control, which would identify missing identifiers after records had already entered the system.
Preventive controls are generally preferable where the organization controls the point of data creation and the business rule is sufficiently clear. They reduce downstream cleansing, exception handling, reconciliation, and operational rework.
However, making the field technically mandatory is appropriate only if the business requirement genuinely applies to every relevant record. Governance and stewardship should confirm the rule before implementation.
If certain product categories legitimately lack the identifier, the validation should incorporate those conditions instead of enforcing an overly broad requirement.
The Product Identifier may also link the transaction to governed Product Master Data, making integrity and consistency important alongside completeness.
DAMA's Data Quality framework emphasizes defining rules, detecting defects, implementing improvement, and integrating quality with Governance and Master Data disciplines rather than relying solely on after-the- fact cleansing.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Completeness; Data Quality Improvement Lifecycle; Chapter 10 - Product Master Data; Governance.
NEW QUESTION # 50
Reference data is often a list of code values with their full names. One example is:
Answer: E
Explanation:
Country codes associated with country names are a classic example of Reference Data. DAMA-DMBOK2 defines Reference Data as data used to characterize or classify other data or to relate organizational data to externally defined information. The simplest reference-data structure consists of a code and its corresponding description. DMBOK2 specifically uses geographic and standards-based examples, including country codes such as DE, US, and TR.
A code such as GB therefore acts as a standardized machine-processable value, while "United Kingdom" supplies the human-readable meaning. Such code lists are reused across applications, integrations, reporting platforms, Master Data systems, and analytical environments.
Reference Data quality is important because inconsistent code sets can create widespread downstream defects.
If different systems use incompatible country codes, the organization may experience failed integrations, incorrect aggregation, duplicate mappings, reporting discrepancies, and inconsistent interpretation. Data Governance should therefore establish authoritative sources and stewardship responsibilities for important reference domains.
Metadata Management records the meaning, provenance, permitted values, relationships, and mappings between code sets. Master Data Management frequently consumes these governed codes to classify master entities such as customers, suppliers, products, and locations.
The other choices describe relationships or transformations, but they do not represent the straightforward code- value/description list structure identified by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data; Reference Lists; Code Sets; Authoritative Sources; Chapter 13 - Validity, Consistency and Integrity.
NEW QUESTION # 51
The search function associated with a document management store is failing to return known artefacts. This is due to a failure of:
Answer: A
Explanation:
A document-management search function depends heavily on metadata to identify, index, classify, and retrieve stored content. If known artefacts exist in the repository but cannot be found through search, inadequate or incorrectly maintained metadata is the most direct explanation.
DAMA-DMBOK2 distinguishes document content from the metadata that describes it. Typical descriptive metadata includes title, author, subject, keywords, document type, creation date, classification, and other characteristics used by retrieval mechanisms. Administrative and structural metadata may additionally support lifecycle management, versioning, access control, and relationships among document components. Without sufficiently accurate and complete metadata, the repository may physically contain a document while users remain unable to discover it.
This is also a Data Quality problem. Metadata itself is data and must satisfy quality expectations such as completeness, validity, consistency, and accuracy. A missing subject classification, incorrect document type, or inconsistent keyword convention can directly reduce findability.
Public access is neither required nor desirable for all documents, especially where confidential information exists. Business Intelligence is unrelated to basic document discovery, and Data Quality metrics alone do not make a document searchable unless the underlying metadata is correctly populated.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Metadata Management; Descriptive Metadata; Search and Retrieval; Chapter 13 - Metadata Quality and Fitness for Purpose.
NEW QUESTION # 52
What is a steward?
Answer: E
Explanation:
In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: "A steward is a person whose job it is to manage the property of another person." It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.
A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.
The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.
A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 - Data Quality Governance and Responsibilities.
NEW QUESTION # 53
A report displaying birth date contains possible, but incorrect values. What is a possible explanation?
Answer: E
Explanation:
The critical wording is "possible, but incorrect values." This describes values that satisfy basic syntactic or domain validation-they look like legitimate dates-but do not accurately represent the real-world attribute defined by the field.
If two systems contribute data and one maps marriage date into the birth-date field, the resulting values can be perfectly valid calendar dates while being semantically incorrect as birth dates. This is principally an Accuracy defect, because DAMA defines accuracy in terms of how correctly data represents the real-world object or event it is intended to describe. It may also expose a consistency and integration-mapping problem between source systems. DAMA's quality framework distinguishes accuracy from completeness: data can be populated and formally valid while still being factually wrong.
Missing values would primarily produce a Completeness defect rather than populated-but-incorrect values.
Two correctly mapped systems would not inherently explain the problem. An offset or technical date representation could create transformation problems, but the scenario most directly illustrates semantic mis- mapping between data elements.
The appropriate remediation is therefore not simple cleansing alone. Metadata mappings, source-to-target specifications, lineage, business definitions, and integration rules should be corrected at the root cause.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy, Completeness and Consistency; Root-Cause Remediation; Data Profiling; Metadata Management; Data Integration and Interoperability.
NEW QUESTION # 54
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