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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Concepts and Principles | 15% | - Data quality dimensions - Definition and importance of data quality - Data quality in DMBOK framework |
| Data Quality Strategy and Governance | 20% | - Integration with data governance - Data quality strategy and policy - Roles and responsibilities |
| Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
| Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Data quality in lifecycle management - Business value and ROI |
| Data Quality Improvement | 20% | - Root cause analysis - Monitoring and continuous improvement - Data cleansing and standardization |
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NEW QUESTION # 121
The main difference between a System of Record and a System of Reference is:
Answer: A
Explanation:
DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption.
A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.
Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.
This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.
Option D is incorrect because "System of Record" and "System of Reference" do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.
Reference Topics: DAMA-DMBOK2 Chapter 10 - System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 - Data Quality and Reference/Master Data Management.
NEW QUESTION # 122
Periodic archiving of transaction data from a production CRM system is critical for:
Answer: A
Explanation:
Periodic archiving is critical for maintaining database performance. As a production CRM accumulates historical transactions, active tables and indexes can become increasingly large. This increases storage consumption, backup duration, index-maintenance overhead, and the quantity of data that database engines must process during operational queries.
DAMA-DMBOK2 treats archiving as an important Data Storage and Operations activity. Historical information that remains subject to retention requirements but is no longer frequently needed for operational processing can be moved to suitable archival storage. DAMA-aligned guidance for this scenario specifically links periodic transaction archiving with maintaining production database performance.
Archiving is not the same as arbitrary deletion. Retention policies, legal obligations, recovery requirements, auditability, and business value determine how long data must remain accessible and where it should be stored. The archive must also be recoverable and appropriately secured.
Data Quality implications include maintaining integrity and traceability during migration to the archive.
Records should remain complete, relationships should be preserved, and metadata should indicate retention status and archival location.
Providing reporting sources or managing deleted customers may be secondary considerations, but neither is the principal purpose described in the question.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Data Storage and Operations; Archiving; Database Performance; Retention; Chapter 13 - Integrity and Historical Data.
NEW QUESTION # 123
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: D
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 124
What is one of the Data Architecture artifacts which are usually captured within development projects, and then standardised and managed by data architects?
Answer: B
Explanation:
The correct answer is Data models. DAMA-DMBOK2 explicitly explains that data models and other Data Architecture artifacts are commonly created or captured within development projects and are subsequently standardized and managed by Data Architects.
Development projects routinely produce conceptual, logical, and physical representations of the data required by a solution. If each project develops those structures independently without architectural review, inconsistent terminology, duplicated entities, conflicting definitions, and incompatible relationship structures can accumulate across the enterprise. Data Architects therefore review project models, reconcile them with enterprise standards, and identify structures suitable for reuse.
An Enterprise Data Model is itself an important architectural artifact, but the wording of the question refers specifically to artifacts typically generated in individual development projects and later standardized. The DMBOK2 passage uses data models in precisely that context.
This process has a direct Data Quality benefit. Standardized models improve consistency of definitions, domains, keys, relationships, and constraints. They also provide metadata from which integrity, uniqueness, validity, and completeness rules can be derived.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Architectural Artifacts; Development Projects; Chapter 5 - Data Modeling and Design; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 125
A CRM contains separate records for "Jonathan Smith", "Jon Smith", and "J. Smith". Investigation confirms that all three records represent the same customer. Which Data Quality dimension is primarily affected?
Answer: A
Explanation:
The primary issue is Uniqueness because more than one record represents the same real-world entity.
Uniqueness evaluates whether unintended duplicate instances exist within a dataset or across integrated datasets.
Duplicate customer records can arise because of spelling variation, abbreviations, different contact details, weak matching rules, or inconsistent identifier capture. Exact duplicate detection alone is therefore insufficient. Effective entity resolution frequently requires probabilistic or deterministic matching across names, addresses, identifiers, telephone numbers, and other attributes.
DAMA-aligned Data Quality frameworks define uniqueness in terms of avoiding duplicate representation of the same entity. They also recognize that duplicate records may differ in individual attribute values while still representing the same person or object.
The issue is closely related to Master Data Management. An MDM process may match the three source records, establish that they belong to one customer, and apply survivorship rules to construct an authoritative master representation.
The correct remediation should also address the source process that allowed duplicates to be created; otherwise cleansing will merely remove symptoms while new duplicates continue to appear.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Uniqueness; Matching; De-duplication; Chapter 10 - Master Data Management; Entity Resolution.
NEW QUESTION # 126
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