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| Section | Objectives |
|---|---|
| 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) |
| Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
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NEW QUESTION # 60
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
Answer: B
Explanation:
The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.
Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.
The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.
This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.
The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.
Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.
Reference Topics: DAMA-DMBOK2 - Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.
NEW QUESTION # 61
Mapping requirements and rules for moving data from source to target enables:
Answer: E
Explanation:
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.
NEW QUESTION # 62
The Data Architecture workstream on results produces:
Answer: D
Explanation:
Within DAMA-DMBOK2, establishing a Data Architecture practice is divided into several complementary workstreams. The Results workstream is specifically responsible for producing Data Architecture artifacts within an overall roadmap. The other answer choices correspond to different aspects of the architecture practice rather than to the Results workstream itself.
DMBOK2 distinguishes the workstreams clearly: Strategy selects frameworks and develops the roadmap; Acceptance and Culture addresses awareness and behavioural change; Organization assigns architecture accountability and responsibility; Working Methods establish practices and coordinate architecture work with development initiatives; and Results produces the architectural artifacts that collectively realize the roadmap.
Typical artifacts include enterprise data models, high-level data-flow designs, architectural standards, transition-state specifications, and related metadata. These artifacts represent both current and target architectural states and allow individual projects to align with enterprise direction.
The Data Quality connection is substantial because architectural artifacts identify authoritative sources, integration points, critical data flows, entities, relationships, and dependencies. Those structures provide the context needed to establish validity, integrity, consistency, lineage, and quality-monitoring requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Establish Data Architecture Practice; Results Workstream; Data Architecture Artifacts; Architecture Roadmap; Chapter 13 - Data Quality Dependencies.
NEW QUESTION # 63
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Answer: B
Explanation:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
NEW QUESTION # 64
Data Staging areas are populated from source databases using extract functions, transformed and then:
Answer: E
Explanation:
The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse.
DAMA-oriented training material presents this exact progression.
The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.
From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.
Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 - Profiling, Cleansing, Standardization and Validation.
NEW QUESTION # 65
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