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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Governance | 15% | - Policies and Procedures - Data Stewardship - Continuous Monitoring - Data Quality Roles and Responsibilities |
| Topic 2: Data Quality Improvement | 20% | - Standardization - Data Enrichment - Validation Rules - Data Cleansing Techniques |
| Topic 3: Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality - Data Quality in the Data Lifecycle |
| Topic 4: Data Quality Frameworks and Standards | 20% | - Other Industry Standards - DAMA DMBOK Framework - ISO 8000 (Data Quality) |
| Topic 5: Data Quality Assessment and Measurement | 25% | - Benchmarking - Root Cause Analysis - Profiling Techniques - Metrics and KPIs |
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The DAMA DQ-1220 certification exam is one of the valuable credentials designed to demonstrate a candidate's technical expertise in information technology. They can remain current and competitive in the highly competitive market with the DQ-1220 certificate. For novices as well as seasoned professionals, the Data Quality Questions provide an excellent opportunity to not only validate their skills but also advance their careers.
NEW QUESTION # 23
How is Data Governance defined?
Answer: B
Explanation:
DAMA-DMBOK2 defines Data Governance as the exercise of authority and control over the management of data assets. This definition distinguishes governance from the operational execution of Data Management.
Independent literature describing DAMA's framework uses the same core formulation, and the DAMA Wheel places Data Governance across the other knowledge areas through this authority-and-control function.
Governance establishes decision rights, accountability, policies, standards, stewardship, issue escalation, compliance expectations, and mechanisms for monitoring whether data is being managed appropriately. It determines who is authorized to make decisions and what rules must be followed; individual Data Management knowledge areas then execute the corresponding operational activities.
Option E is closer to the broad definition of Data Management, because it refers to planning, implementation, and control across the information lifecycle. Option A describes an assessment or maturity activity. Option B describes selected governance benefits rather than its definition, while option C characterizes a collection of functions.
For Data Quality, governance provides the authority needed to define critical data, approve quality requirements and thresholds, assign accountability, prioritize remediation, and enforce standards. Without this authority structure, profiling may identify defects but the organization may lack the decision mechanisms required to correct their underlying causes.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Definition; Authority and Control; Policies and Standards; Chapter 13 - Data Quality Governance.
NEW QUESTION # 24
Well executed metadata management enables:
Answer: A
Explanation:
The fundamental outcome of well-executed Metadata Management is a consistent understanding of data resources. DAMA-DMBOK2 states this directly: effective Metadata Management enables consistent understanding of data resources and supports more efficient development across organizational boundaries.
Metadata supplies context that raw data cannot provide by itself. It explains business meaning, technical structures, valid domains, calculations, transformations, ownership, lineage, systems of record, access restrictions, known quality issues, and update schedules. DMBOK2 also identifies Data Quality rules and measurement results as forms of business metadata.
Consequently, Metadata Management is broader than database design, dimensional reporting, authorization, or Data Modeling. Each of those activities consumes metadata, but none represents its complete objective.
This capability is essential to Data Quality. A dataset cannot be meaningfully evaluated for fitness for purpose unless users know what its fields mean, where the data originated, how values were transformed, which business rules apply, and which source is authoritative. Metadata therefore provides the semantic and technical framework required to define and interpret Data Quality measurements consistently.
DAMA's current DMBOK2 revision also explicitly strengthens the relationship between Data Quality and Metadata Management.
Reference Topics: DAMA-DMBOK2 Metadata Management - Goals and Principles; Business Metadata; Technical Metadata; Data Lineage; Chapter 13 - Data Quality and Metadata Management.
NEW QUESTION # 25
A Data Quality team finds that 78% of customer complaints are caused by four defect categories out of twenty recorded categories. Which tool is most appropriate for prioritizing improvement activity?
Answer: D
Explanation:
Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.
The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.
The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship.
Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.
Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.
Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.
NEW QUESTION # 26
A lineage data tool provides:
Answer: A
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 # 27
The need to manage data movement efficiently is a primary driver for:
Answer: B
Explanation:
The need to manage data movement efficiently is a primary business driver for Data Integration and Interoperability (DII). DAMA-DMBOK2 states this directly. Modern organizations operate hundreds or thousands of databases, applications, files, services, data stores, external interfaces, and analytical platforms.
Data must continually move among these environments, often across organizational boundaries.
Without disciplined integration management, data movement becomes fragmented, expensive, difficult to monitor, and highly dependent on duplicated point-to-point interfaces. DII addresses this problem by establishing controlled mechanisms for extraction, transformation, messaging, replication, orchestration, APIs, data virtualization, and other forms of information exchange.
From a Data Quality perspective, every movement of data presents an opportunity either to preserve quality or to damage it. Source-to-target mappings must maintain semantic meaning, transformations must be controlled, reference values must remain consistent, and lineage must show how values were altered.
Metadata Management therefore records mappings, interface definitions, transformation rules, and lineage.
Master Data Management uses integration mechanisms to distribute governed master and reference data consistently between systems.
Data Warehousing is a major consumer of integration capabilities, but the broader discipline whose explicit driver is efficient movement across systems is Data Integration and Interoperability.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Business Drivers; Data Integration and Interoperability; Data Movement; Chapter 13 - Consistency, Integrity and Transformation Quality.
NEW QUESTION # 28
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