The DAMA DQ-1220 is available in three easy-to-use forms. The first one is DAMA DQ-1220 dumps PDF format. It is printable and portable. You can print Data Quality (DQ-1220) questions PDF or access them via your smartphones, tablets, and laptops. The PDF format can be used anywhere and is essential for students who like to learn on the go.
DAMA DQ-1220 Certification Exam covers a wide range of topics related to data quality management, including data profiling, data quality assessment, data cleansing, data governance, and data stewardship. DQ-1220 exam is available in both online and in-person formats and is open to anyone interested in earning a credential in data quality management. Successful completion of the exam demonstrates an individual's ability to effectively manage data quality in a variety of organizational contexts, making them a valuable asset to any organization seeking to improve their data quality practices.
To be eligible for the DAMA DQ-1220 Certification Exam, candidates must have a minimum of two years of experience in data management or a related field. They must also complete a training program that covers the exam's content, such as DAMA's Data Quality Fundamentals course. DQ-1220 exam itself is a computer-based test that consists of 100 multiple-choice questions. Candidates have two hours to complete the exam, and a passing score of 70% is required.
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To prepare for the DAMA DQ-1220 exam, individuals can take data quality management courses, participate in data quality management projects, and join professional organizations such as the Data Management Association (DAMA). Moreover, individuals should review and understand the DAMA DQ-1220 Exam Preparation guide and sample questions, ensuring that they have understood all topics in the exam.
NEW QUESTION # 47
A team uses a fishbone diagram during investigation of recurring missing customer email addresses. What is the primary purpose of the diagram?
Answer: C
Explanation:
A fishbone diagram, also known as an Ishikawa or cause-and-effect diagram, is used to organize and explore potential root causes of a problem.
For recurring missing email addresses, categories might include People, Process, Technology, Data, Policy, Training, and External Sources. Potential causes could include optional form design, unclear business requirements, API mapping defects, missing supplier data, or inconsistent onboarding procedures.
The technique helps prevent teams from prematurely assuming that the most visible symptom is the actual cause. It encourages structured investigation across multiple dimensions of the process.
Once hypotheses are identified, evidence should be collected to confirm or reject them. The diagram itself does not prove causation.
In Data Quality programs, root-cause analysis is essential because repeated cleansing without process correction allows defects to recur. DAMA's Chapter 13 revision explicitly retains and strengthens the treatment of common causes and the improvement lifecycle.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Cause-and-Effect Diagram; Issue Management; Continuous Improvement.
NEW QUESTION # 48
According to the ISO/IEC 42010:2007 Software and Systems Engineering -Architecture Description, which of the following describes the definition of architecture:
Answer: B
Explanation:
The correct definition is the fundamental organisation of a system, and the principles governing its design and evolution. DAMA-DMBOK2 incorporates the ISO/IEC 42010 architectural concept when establishing the theoretical basis for Data Architecture.
The fuller ISO formulation describes architecture in terms of the fundamental organization of a system embodied in its components, their relationships to one another and to the environment, together with the principles governing design and evolution. DAMA uses this concept to distinguish true architecture from a collection of diagrams or implementation specifications.
Option E is therefore incomplete: architectural artifacts document architecture, but the artifacts themselves are not the architecture. Option C focuses too narrowly on implementation and maintenance, while options B and D describe potential objectives or constraints rather than the definition.
Applied to enterprise data, this means architecture establishes the high-level organization of data assets, information flows, platforms, relationships, and guiding principles by which the data environment evolves.
Data Quality benefits because architectural principles establish where authoritative information originates, how it moves, how duplication is controlled, and where governance and quality controls must operate.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Architecture Definition; ISO/IEC 42010; Enterprise Data Architecture; Architectural Principles; Chapter 13 - Quality by Design.
NEW QUESTION # 49
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: C
Explanation:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
NEW QUESTION # 50
A customer's date of birth is stored as 12/04/1980. The value conforms to the database datatype and accepted date format, but the customer's verified date of birth is 21/04/1980. Which Data Quality dimension is primarily violated?
Answer: A
Explanation:
The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real- world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.
This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible-for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.
The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.
The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.
NEW QUESTION # 51
Descriptive metadata describes a resource, enabling its identification and retrieval.
Answer: B
Explanation:
DAMA-DMBOK2 distinguishes descriptive, structural, and administrative metadata when discussing metadata classifications used outside conventional IT contexts. Descriptive metadata identifies and describes a resource sufficiently to enable discovery and retrieval. DMBOK2 gives title, author, and subject as explicit examples of descriptive metadata.
A book's author, title, and subject therefore answer practical discovery questions: What is the resource? Who created it? What is it about? These attributes allow catalogues, repositories, search tools, and users to identify and retrieve the required resource.
Option A is principally structural metadata. DMBOK2 specifically identifies the number of pages and chapters as examples describing relationships within or among the components of a resource. Option D represents administrative metadata because version numbers and archive dates help manage the resource throughout its lifecycle.
The distinction is important to Data Quality because metadata itself must have appropriate completeness, consistency, currency, and accuracy. Incorrect descriptive metadata can make a valid data asset effectively undiscoverable or lead users to select an inappropriate source.
Metadata Management therefore supports Data Quality by giving data consumers reliable definitions, context, provenance, ownership, lineage, and usage information.
Reference Topics: DAMA-DMBOK2 Metadata Management - Descriptive Metadata; Structural Metadata; Administrative Metadata; Metadata Quality; Chapter 13 - Data Quality and Metadata Management.
NEW QUESTION # 52
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