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DAMA DQ-1220 Exam is considered a highly respected certification in the field of data quality management. It is recognized globally and demonstrates a high level of expertise in the area of data quality. DQ-1220 exam consists of 100 multiple choice questions and is timed at 2 hours. It requires a passing score of 70% or higher. Upon passing the exam, participants receive a certificate that can help to advance their careers and increase their earning potential. Overall, the DAMA DQ-1220 Exam is an excellent opportunity for professionals to demonstrate their knowledge and expertise in the field of data quality management.
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DAMA DQ-1220 exam covers various topics related to data quality, including data profiling, data cleansing, data governance, data integration, and data quality measurement. It is designed to test an individual's ability to identify, analyze, and resolve data quality issues, as well as to implement data quality solutions. DQ-1220 Exam is ideal for individuals who are involved in data management, data governance, data analytics, data quality assurance, and related fields. Passing the exam demonstrates a high level of expertise in data quality and can lead to better career opportunities in the field.
NEW QUESTION # 84
A form permits users to enter any text into a field that should contain an ISO country code. What is the strongest preventive Data Quality control?
Answer: D
Explanation:
The strongest preventive control is to restrict data entry to the approved country-code domain. Prevention at the point of creation is generally more effective and less costly than repeatedly correcting invalid values downstream.
A controlled drop-down list, validated API, or reference-domain lookup ensures that users can select only permitted codes. This directly supports Validity and improves Consistency across applications.
The rule should be linked to centrally governed Reference Data rather than hard-coded independently in each application. That allows changes to the permitted domain to be managed systematically.
Downstream cleansing remains useful for historical defects or external data, but it should not be the primary control when the organization controls the source-entry process.
DAMA's framework links Reference/Master Data Management to consistency of shared values and Chapter
13 to defining and monitoring Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Validity; Data Creation; Chapter
10 - Reference Data; Controlled Domains.
NEW QUESTION # 85
A lineage data tool provides:
Answer: C
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 # 86
Integrating data security with document and content management knowledge areas, guides the implementation of:
Answer: E
Explanation:
Document and Content Management focuses on information stored outside conventional relational databases, including documents, images, multimedia, email, and other semi-structured or unstructured assets. Integrating this knowledge area with Data Security therefore guides the implementation of appropriate access and authorization controls for unstructured data.
DAMA's framework treats security as a cross-cutting discipline rather than something applicable only to database tables. Organizational documents can contain personally identifiable information, intellectual property, contracts, financial records, or other confidential material and therefore require the same disciplined approach to authentication, authorization, classification, retention, and monitoring as structured data. DAMA- aligned references explicitly identify appropriate access and authorization to unstructured data as the relevant interaction between these knowledge areas.
Metadata is also important because document classifications, ownership, retention category, confidentiality level, and permitted audiences provide the information needed to enforce controls.
Option A refers to structured data and therefore misses the specific contribution of Document and Content Management. Fitness-for-purpose measurement belongs primarily to Data Quality, while data-mart privacy addresses a narrower structured analytical environment.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Chapter 9 - Document and Content Management; Unstructured Data; Access Control; Authorization; Information Classification.
NEW QUESTION # 87
What is Data Stewardship?
Answer: E
Explanation:
DAMA-DMBOK2 defines Data Stewardship in terms of accountability and responsibility for data and for the processes that ensure its effective control and use. The wording in option E closely matches the DMBOK2 definition: stewardship describes accountability and responsibility for data and processes that ensure the effective control and use of data assets.
Stewardship may be formalized through named positions or responsibilities embedded within existing business roles. Its practical scope commonly includes managing business terminology, defining valid values and business rules, establishing or approving Data Quality requirements, resolving data issues, applying standards, and supporting governance decisions.
Option C is too narrow and also confuses stewardship with the broader policy-setting responsibilities of Data Governance. Stewards participate in developing and implementing policies and standards, but stewardship is not merely a policy-creation role. Similarly, it is not a privacy technology function or a project-management construct.
Within Data Quality Management, Data Stewards are critical because quality must be defined relative to business requirements. They help determine what "fit for purpose" means, establish acceptable thresholds, prioritize defects according to business impact, and participate in root-cause remediation.
Metadata Management records stewardship decisions, while Master Data Management uses those decisions to govern shared enterprise entities.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Steward Responsibilities; Business Glossary; Chapter 13 - Data Quality Governance and Issue Management.
NEW QUESTION # 88
A Data Quality rule states that every active supplier must have a valid tax identifier. Who should approve whether the rule is mandatory for the Supplier domain?
Answer: C
Explanation:
Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.
A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.
The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.
This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.
DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance and Stewardship; Chapter 13 - Data Quality Rules; Metadata Management; Supplier Master Data.
NEW QUESTION # 89
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