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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 2: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Topic 3: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Topic 4: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Topic 5: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
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NEW QUESTION # 70
Obfuscation or redaction of data is the practice of:
Answer: C
Explanation:
DAMA-DMBOK2 defines obfuscation or redaction as making information anonymous or removing sensitive information. The purpose is to reduce the risk that protected, confidential, or personally identifiable information can be exposed to users or processes that do not require access to the original values.
Obfuscation can involve masking, substitution, shuffling, temporal variation, partial display, or other methods that change what the recipient sees while preserving sufficient utility for the intended activity. For example, a customer-service agent may see only the final digits of an account identifier, or a development team may receive realistic but anonymized production-derived test data.
Redaction may remove information entirely from a representation when there is no legitimate requirement to expose it.
The technique is therefore a Data Security and privacy control, not database compression, publication, or classification.
There is also an important Data Quality consideration. Masked or obfuscated data used for testing must remain structurally valid and retain required relationships so that applications behave realistically. Metadata should indicate that a dataset has been transformed for privacy purposes so consumers do not mistake masked values for authoritative production data.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Obfuscation; Redaction; Data Masking; Privacy; Sensitive Information; Chapter 13 - Fitness for Purpose and Controlled Transformation.
NEW QUESTION # 71
A relationship that allows an address to be used by multiple people, and each person can have multiple addresses, can be resolved:
Answer: A
Explanation:
The scenario describes a many-to-many relationship: one Person may use several Addresses, while one Address may be associated with several Persons. In relational modelling, this should be resolved using an associative entity-here, Person Address Usage-between Person and Address. The original many-to-many relationship is thereby replaced with two one-to-many relationships. DAMA-oriented references identify this approach directly under the addition of associative entities.
The associative entity normally contains foreign keys referencing both parent entities and may also contain attributes describing the association itself, such as address type, usage purpose, effective date, end date, or primary-address indicator.
Simply modifying the primary keys of Person or Address does not resolve the semantic relationship.
Changing foreign-key role names also changes terminology rather than cardinality. The term "partnership entity" is not the appropriate modelling construct.
This design has important Data Quality consequences. It permits explicit integrity constraints between the association and both parent entities, prevents ambiguous repeated columns, and allows the organization to validate rules such as valid effective periods and permitted address-use types.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Relationships; Cardinality; Associative Entities; Resolving Many-to-Many Relationships; Chapter 13 - Referential Integrity and Consistency.
NEW QUESTION # 72
An effective Data Governance communication program should include the following:
Answer: A
Explanation:
An effective Data Governance communication program should employ multiple complementary communication mechanisms, making All answers correct. Governance changes how people define, create, use, approve, and resolve issues with data; consequently, sustained adoption requires more than publishing policies.
Regular newsletters keep stakeholders aware of progress, decisions, metrics, and upcoming activities. A Data Governance Portal provides a persistent location for policies, standards, stewardship information, glossaries, issue processes, and supporting materials. Custom training develops the capabilities required for individuals to understand their specific governance responsibilities. Informal networking events help establish relationships across business and technical groups, which is particularly important when resolving data ownership and definition conflicts.
DAMA-DMBOK2 treats communication and organizational change as core implementation considerations because governance depends on participation across functions rather than on a single technical team.
Published CDMP material for this item identifies the combined response-newsletters, portal, training, and networking-as the intended answer.
For Data Quality, communication ensures that quality definitions, issue-management procedures, stewardship responsibilities, thresholds, and remediation decisions are understood and consistently applied.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Communications; Organizational Change; Training; Data Governance Portal; Stewardship Engagement; Chapter 13 - Data Quality Culture.
NEW QUESTION # 73
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 # 74
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 # 75
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