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| Section | Objectives |
|---|---|
| Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
| Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
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101. Frage
Mapping requirements and rules for moving data from source to target enables:
Antwort: C
Begründung:
Source-to-target mapping enables Transformation. DAMA-DMBOK2 treats mapping as closely synonymous with transformation because a mapping defines how data in one source structure will be converted into the structure, format, representation, or value required by the target.
A mapping specification typically identifies the source attribute, target attribute, extraction conditions, target population rules, intermediate staging transformations, calculations, lookup requirements, and any changes required to make the source data conform to the target representation. DMBOK2 specifically explains that mapping sources to targets involves defining the rules for transforming information from one location and format into another.
Extraction simply retrieves data from the source. Loading places data into the target. Transformation is the activity that applies structural, syntactic, semantic, or value-level modifications between those stages.
The Data Quality connection is substantial. Mappings may standardize dates, convert units, harmonize codes, resolve reference values, remove duplicates, or enforce business rules. If mapping metadata is incomplete or incorrect, the transformation process can introduce rather than correct quality defects.
For this reason, source-to-target mapping should be governed, version-controlled, documented as metadata, traceable through lineage, and validated against agreed business definitions and Data Quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Map Data Sources to Targets; Transformation; ETL
/ELT; Metadata Lineage; Chapter 13 - Data Cleansing and Standardization.
102. Frage
Which of the following activities is NOT a way that enterprise data architecture influences the scope boundaries of projects?
Antwort: A
Begründung:
Ensuring enterprise business processes are effectively documented is not primarily a Data Architecture mechanism for defining project scope boundaries. That responsibility belongs more directly to Business Architecture, process management, and business-analysis disciplines.
DAMA-DMBOK2 explains that Enterprise Data Architecture influences projects by defining enterprise data requirements, reviewing project data designs, determining lineage impacts, controlling unnecessary replication, enforcing Data Architecture standards, and guiding technology and renewal decisions.
Consequently, options A, B, D, and E all represent valid ways architecture can constrain or guide project scope. A design review checks that local solutions remain compatible with enterprise strategy. Standards prevent individual projects from creating incompatible structures. Replication controls limit uncontrolled proliferation of redundant data. Enterprise data requirements ensure that projects account for information needs beyond their immediate application boundaries.
Documenting business processes remains important because processes produce and consume data, but comprehensive process documentation is not itself a primary Enterprise Data Architecture scope-control activity.
From a Data Quality perspective, architectural influence prevents project-level decisions from creating duplicated authoritative sources, inconsistent definitions, undocumented lineage, or uncontrolled transformations.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Manage Enterprise Requirements within Projects; Architecture Governance; Project Scope Boundaries; Data Replication; Chapter 13 - Consistency and Lineage.
103. Frage
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Antwort: B
Begründung:
The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.
This reasoning is deceptive. The acquisition cost of storage is only one component of total information- management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.
From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.
A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 - Data Quality and Obsolete Data.
104. Frage
A report displaying birth date contains possible, but incorrect values. What is a possible explanation?
Antwort: A
Begründung:
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.
105. Frage
What are the types of models that are modelled in data models?
Antwort: C
Begründung:
DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.
Category information classifies things-for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.
Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.
This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.
Data models also generate important metadata-definitions, relationships, domains and constraints-which becomes the basis for measurable Data Quality rules.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Types of Data that are Modeled; Chapter 10 - Reference and Master Data; Chapter 13 - Data Quality Rules and Dimensions; Metadata Management.
106. Frage
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