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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Governance | 15% | - Policies and Procedures - Continuous Monitoring - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - Other Industry Standards - ISO 8000 (Data Quality) |
| Data Quality Assessment and Measurement | 25% | - Benchmarking - Metrics and KPIs - Profiling Techniques - Root Cause Analysis |
| Data Quality Improvement | 20% | - Data Enrichment - Data Cleansing Techniques - Standardization - Validation Rules |
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
>> DAMA DQ-1220 Reliable Study Plan <<
At BraindumpQuiz, we are aware that every applicant of the Data Quality (DQ-1220) examination is different. We know that everyone has a distinct learning style, situations, and set of goals, therefore we offer DAMA DQ-1220 updated exam preparation material in three easy-to-use formats to accommodate every exam applicant's needs. This article will go over the three formats of the Data Quality (DQ-1220) practice material that we offer.
NEW QUESTION # 40
Mapping requirements and rules for moving data from source to target enables:
Answer: C
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 # 41
The search function associated with a document management store is failing to return known artefacts. This is due to a failure of:
Answer: E
Explanation:
A document-management search function depends heavily on metadata to identify, index, classify, and retrieve stored content. If known artefacts exist in the repository but cannot be found through search, inadequate or incorrectly maintained metadata is the most direct explanation.
DAMA-DMBOK2 distinguishes document content from the metadata that describes it. Typical descriptive metadata includes title, author, subject, keywords, document type, creation date, classification, and other characteristics used by retrieval mechanisms. Administrative and structural metadata may additionally support lifecycle management, versioning, access control, and relationships among document components. Without sufficiently accurate and complete metadata, the repository may physically contain a document while users remain unable to discover it.
This is also a Data Quality problem. Metadata itself is data and must satisfy quality expectations such as completeness, validity, consistency, and accuracy. A missing subject classification, incorrect document type, or inconsistent keyword convention can directly reduce findability.
Public access is neither required nor desirable for all documents, especially where confidential information exists. Business Intelligence is unrelated to basic document discovery, and Data Quality metrics alone do not make a document searchable unless the underlying metadata is correctly populated.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Metadata Management; Descriptive Metadata; Search and Retrieval; Chapter 13 - Metadata Quality and Fitness for Purpose.
NEW QUESTION # 42
Who should normally define the business meaning and acceptable quality requirements for a critical customer attribute?
Answer: B
Explanation:
A Business Data Steward, working with relevant business stakeholders and governance bodies, is the appropriate role to define or coordinate business meaning and quality expectations.
Quality requirements cannot be derived solely from database structures. The organization must understand what the attribute means, how it is used, what values are acceptable, which source is authoritative, and what consequences arise when it is wrong.
Data Stewards bridge business knowledge and formal Data Management controls. They commonly participate in maintaining glossary definitions, defining business rules, resolving issues, clarifying ownership, and establishing Data Quality expectations.
Technical specialists contribute implementation knowledge. A DBA may identify datatype constraints, while an integration specialist can implement transformations. Neither should independently determine business meaning.
DAMA's public framework describes Metadata Management as supporting definitions, lineage, and governance, while Reference and Master Data Management ensures consistency in shared core entities.
The strongest operating model therefore combines business accountability with technical execution.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Chapter 11 - Business Metadata; Chapter 13 - Data Quality Requirements; Critical Data Elements.
NEW QUESTION # 43
In 2009, ARMA International published GARP for managing records and information. GARP stands for:
Answer: E
Explanation:
GARP stands for Generally Accepted Recordkeeping Principles. ARMA International developed the framework to describe the characteristics of an effective records and information-management program. The principles address accountability, transparency, integrity, protection, compliance, availability, retention, and disposition.
These principles align closely with DAMA-DMBOK2's treatment of Document and Content Management.
Information must be created, organized, protected, maintained, retrieved, retained, and ultimately disposed of according to business, legal, regulatory, and historical requirements. Records management is therefore not simply about long-term storage. It establishes controls throughout the information lifecycle.
For example, the Integrity principle addresses the authenticity and reliability of records. Availability requires information to be retrievable accurately and efficiently. Retention requires organizations to keep records for justified periods, while Disposition governs appropriate final handling after those obligations expire.
These controls interact directly with Data Quality. Records that cannot be found, trusted, interpreted, or demonstrated to be authentic are not fit for their intended purpose even if they physically exist.
Metadata Management supplies classification, retention, provenance, ownership, and lifecycle metadata necessary to implement these principles consistently.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Records Management; GARP; Retention; Disposition; Integrity; Metadata Management.
NEW QUESTION # 44
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
Answer: B
Explanation:
The organization should determine the root cause and authoritative remediation point before multiple teams independently modify downstream copies.
If a defective value originates in a source system and is distributed to five downstream applications, correcting those five copies independently may provide temporary relief but leaves the upstream defect intact.
The incorrect value may simply be redistributed again.
Lineage should be used to trace the defect through source systems, transformations, integration layers, Master Data hubs, warehouses, and reports. Once the origin is understood, governance can determine which system or process is authoritative and where correction should occur.
This approach minimizes inconsistent local fixes and reduces the risk that different teams apply incompatible interpretations of the same issue.
DAMA's Chapter 13 revision explicitly strengthens the relationship between Data Quality and Metadata Management, Reference/Master Data Management, Modeling, and Data Integration. Those connections are precisely what enable cross-system issue resolution.
Downstream correction may still be required after the authoritative source is fixed, but it should be coordinated.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Issue Management; Metadata Lineage; Authoritative Sources; Data Integration.
NEW QUESTION # 45
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