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| Section | Weight | Objectives |
|---|---|---|
| Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Data Quality Improvement | 20% | - Data cleansing and standardization - Monitoring and continuous improvement - Root cause analysis |
| Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
| Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
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NEW QUESTION # 29
A lineage data tool provides:
Answer: B
Explanation:
A Data Lineage tool provides the capture and maintenance of source structures for each attribute on the data model. Lineage connects modeled data elements to their originating structures and records how those elements move and transform through downstream systems. DAMA-related practice material identifies this specific capability as the correct function of lineage tooling.
At attribute level, lineage may identify a source table and column, transformation logic, staging structures, intermediate mappings, target columns, and ultimately the report or analytical object in which the value appears. This information allows organizations to answer fundamental questions such as: Where did this value originate? What transformations were applied? Which systems depend on this field? What downstream assets will be affected if its definition changes?
For Data Quality, lineage is a critical diagnostic capability. If a value becomes inaccurate after a transformation, lineage helps isolate the point of failure and distinguish source-system defects from integration or reporting defects. It also supports impact analysis before quality-rule changes are deployed.
Metadata Management normally provides the repository and technical structures that preserve lineage.
Governance establishes ownership and documentation standards, while Data Stewards use lineage to understand business impact and coordinate remediation.
The other choices do not describe recognized lineage functions.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Data Architecture; Source- to-Target Mapping; Chapter 13 - Issue Investigation and Root-Cause Analysis.
NEW QUESTION # 30
Which DMBoK knowledge area is most likely responsible for a high percentage of returned mail?
Answer: C
Explanation:
A high percentage of returned mail is primarily a Data Quality problem. Returned correspondence frequently results from inaccurate, incomplete, obsolete, incorrectly formatted, or otherwise unusable address data. The operational symptom therefore indicates that address information is failing to meet its intended business purpose.
Published versions of this DAMA question identify Data Quality as the intended knowledge area. The underlying principle aligns directly with DMBOK2 Chapter 13: quality is assessed relative to the requirements of the business process consuming the data.
Several quality dimensions may contribute. Accuracy concerns whether the address correctly represents the customer's actual location. Completeness concerns missing apartment numbers, postal codes, or address components. Validity evaluates whether values conform to accepted formats and domains. Currency addresses whether a previously correct address has become obsolete. Consistency can also be involved where address information differs between systems.
Reference and Master Data Management may provide customer-master consolidation and standardized geographic reference values, but the direct defect represented by returned mail is poor-quality address data.
A mature response should involve profiling returned-mail records, determining defect patterns and root causes, establishing address-quality rules, correcting affected records, and introducing preventive validation at capture.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Fitness for Purpose; Accuracy; Completeness; Validity; Currency; Profiling; Root-Cause Analysis.
NEW QUESTION # 31
The goals of data storage and operations are:
Answer: E
Explanation:
DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.
Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.
Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.
Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.
Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.
The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 - Data Reliability and Timeliness.
NEW QUESTION # 32
Mapping requirements and rules for moving data from source to target enables:
Answer: A
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 # 33
A relationship that allows an address to be used by multiple people, and each person can have multiple addresses, can be resolved:
Answer: C
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 # 34
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