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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Assessment methods and tools - Defining quality rules and metrics |
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
| Data Quality Improvement | 20% | - Root cause analysis - Data cleansing and standardization - Monitoring and continuous improvement |
| Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Data quality tools and technologies - Business value and ROI |
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
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NEW QUESTION # 36
The creation of overly complex enterprise integration over time is often a symptom of:
Answer: D
Explanation:
The strongest indicator is the uncontrolled proliferation of multiple integration technologies. Enterprise integration environments often become progressively more complex when different projects independently adopt different ETL platforms, messaging products, APIs, middleware technologies, replication mechanisms, file-transfer approaches, and proprietary interfaces.
The problem is architectural fragmentation. Each integration technology introduces its own configuration methods, transformation logic, monitoring mechanisms, metadata, failure handling, security controls, support skills, and operational procedures. As the number of technologies increases, point-to-point dependencies multiply and the organization accumulates technical debt. This makes end-to-end lineage, troubleshooting, change-impact analysis, and consistent application of Data Quality rules significantly harder. The source question itself identifies this specific integration-management issue. DAMA-oriented exam material likewise identifies multiple integration technologies as the characteristic cause of an overly complex integration landscape.
DAMA-DMBOK2 therefore emphasizes managed Data Integration and Interoperability architecture, reusable patterns, common models, governed interfaces, and metadata describing transformations.
From a Data Quality perspective, uncontrolled integration technology can result in inconsistent transformations, duplicated cleansing rules, mismatched reference values, and poorly understood lineage.
Standardization reduces these risks by making movement and transformation processes more transparent and governable.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Integration Architecture; Metadata and Lineage; Chapter 13 - Quality Controls Across Data Movement.
NEW QUESTION # 37
The Data Architecture workstream on results produces:
Answer: E
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 # 38
What are the types of models that are modelled in data models?
Answer: C
Explanation:
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.
NEW QUESTION # 39
A report value is incorrect, and the analyst needs to identify every source field and transformation used to calculate it. Which capability is most important?
Answer: B
Explanation:
The required capability is Data Lineage. Lineage documents how data originates, moves, transforms, and ultimately appears in downstream reports, applications, and analytical products.
When a report value is wrong, lineage allows investigators to trace the value backwards through calculations, mappings, staging structures, integration processes, source tables, and potentially external providers. This prevents teams from guessing where the defect originated.
Metadata Management is the principal discipline responsible for collecting and maintaining this contextual information. DAMA's public framework identifies lineage and definitions as central Metadata Management capabilities.
Lineage is also essential for change-impact analysis. If a source element changes, teams can determine which reports, models, or applications depend upon it.
For Data Quality, lineage supports root-cause analysis and coordinated remediation. A downstream value may be accurate relative to the transformation applied but still wrong because the source or transformation logic was incorrect.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Source-to-Target Mapping; Root-Cause Analysis; Data Integration.
NEW QUESTION # 40
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: C
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 41
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