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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Governance | 15% | - Data Stewardship - Policies and Procedures - Continuous Monitoring - Data Quality Roles and Responsibilities |
| Topic 2: Data Quality Assessment and Measurement | 25% | - Profiling Techniques - Root Cause Analysis - Metrics and KPIs - Benchmarking |
| Topic 3: Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - ISO 8000 (Data Quality) - Other Industry Standards |
| Topic 4: Data Quality Fundamentals | 20% | - Definition of Data Quality - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Topic 5: Data Quality Improvement | 20% | - Data Enrichment - Data Cleansing Techniques - Standardization - Validation Rules |
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質問 # 104
Data Staging areas are populated from source databases using extract functions, transformed and then:
正解：A
解説：
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.
質問 # 105
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
正解：A
解説：
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
質問 # 106
What are the types of models that are modelled in data models?
正解：C
解説：
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.
質問 # 107
Profiling shows that whenever Account_Status is "Closed", Closure_Date is normally populated. However,
4% of closed accounts have a null Closure_Date. What type of profiling has revealed this issue?
正解：C
解説：
This is cross-column dependency profiling. The issue cannot be identified merely by counting null values in Closure_Date because some nulls may be legitimate for active accounts. The defect becomes meaningful only when the relationship between two attributes is examined.
The implied business rule is: if Account_Status = Closed, then Closure_Date must be populated. Dependency profiling examines relationships among values and can reveal conditional completeness, inconsistent combinations, functional dependencies, and other multi-attribute patterns.
The next step is to confirm the inferred pattern with business stakeholders rather than automatically converting it into an enforced rule. There may be exceptional account categories where a closed status legitimately lacks a closure date.
Once approved, the rule should be documented as metadata, assigned to a Data Steward, measured against an agreed threshold, and monitored. Root-cause investigation can then determine whether missing dates arise from source-screen design, process bypass, conversion defects, or integration failures.
DAMA's revised Chapter 13 places Data Quality techniques in a more practical sequence and strengthens their linkage with Data Modeling and Metadata Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Dependency Analysis; Completeness; Business Rules; Metadata Management.
質問 # 108
Before defining a Data Quality metric for a business-critical field, the team should first:
正解：D
解説：
A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.
For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.
The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.
DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.
Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.
Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.
質問 # 109
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