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DAMA DQ-1220 (Data Quality) Exam is a certification exam that focuses on the importance of data quality in today's organizations. Data quality is essential for organizations to make accurate decisions and achieve their business objectives. The DAMA DQ-1220 Exam is designed to help professionals develop their understanding of data quality and become proficient in implementing data quality programs in their organizations.
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To prepare for the exam, candidates can attend training courses offered by DAMA International or other training providers. The training covers the key concepts and best practices related to data quality and provides candidates with the necessary knowledge to pass the exam. Additionally, candidates can use study materials, such as books, online resources, and practice exams, to reinforce their understanding of the topics covered in the exam.
NEW QUESTION # 73
Obfuscation or redaction of data is the practice of:
Answer: D
Explanation:
DAMA-DMBOK2 defines obfuscation or redaction as making information anonymous or removing sensitive information. The purpose is to reduce the risk that protected, confidential, or personally identifiable information can be exposed to users or processes that do not require access to the original values.
Obfuscation can involve masking, substitution, shuffling, temporal variation, partial display, or other methods that change what the recipient sees while preserving sufficient utility for the intended activity. For example, a customer-service agent may see only the final digits of an account identifier, or a development team may receive realistic but anonymized production-derived test data.
Redaction may remove information entirely from a representation when there is no legitimate requirement to expose it.
The technique is therefore a Data Security and privacy control, not database compression, publication, or classification.
There is also an important Data Quality consideration. Masked or obfuscated data used for testing must remain structurally valid and retain required relationships so that applications behave realistically. Metadata should indicate that a dataset has been transformed for privacy purposes so consumers do not mistake masked values for authoritative production data.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Obfuscation; Redaction; Data Masking; Privacy; Sensitive Information; Chapter 13 - Fitness for Purpose and Controlled Transformation.
NEW QUESTION # 74
A CRM contains separate records for "Jonathan Smith", "Jon Smith", and "J. Smith". Investigation confirms that all three records represent the same customer. Which Data Quality dimension is primarily affected?
Answer: A
Explanation:
The primary issue is Uniqueness because more than one record represents the same real-world entity.
Uniqueness evaluates whether unintended duplicate instances exist within a dataset or across integrated datasets.
Duplicate customer records can arise because of spelling variation, abbreviations, different contact details, weak matching rules, or inconsistent identifier capture. Exact duplicate detection alone is therefore insufficient. Effective entity resolution frequently requires probabilistic or deterministic matching across names, addresses, identifiers, telephone numbers, and other attributes.
DAMA-aligned Data Quality frameworks define uniqueness in terms of avoiding duplicate representation of the same entity. They also recognize that duplicate records may differ in individual attribute values while still representing the same person or object.
The issue is closely related to Master Data Management. An MDM process may match the three source records, establish that they belong to one customer, and apply survivorship rules to construct an authoritative master representation.
The correct remediation should also address the source process that allowed duplicates to be created; otherwise cleansing will merely remove symptoms while new duplicates continue to appear.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Uniqueness; Matching; De-duplication; Chapter 10 - Master Data Management; Entity Resolution.
NEW QUESTION # 75
A minimal super key is:
Answer: B
Explanation:
A candidate key is a minimal super key. A super key is any set of attributes sufficient to uniquely identify an entity instance, but it may contain attributes that are unnecessary for uniqueness. A candidate key removes that redundancy: if any attribute is removed from the candidate key, the remaining attributes no longer uniquely identify the entity.
DAMA-DMBOK2 makes this distinction explicitly: a candidate key is a minimal set of one or more attributes identifying an entity instance, and "minimal" means that no subset of the candidate key can perform the same unique-identification function.
Option B is incomplete because a set can uniquely identify an entity while still containing unnecessary attributes; that would qualify as a super key but not necessarily a minimal super key. A surrogate key is different: it is an artificial identifier introduced primarily for technical identification. Foreign-key composition and physical index structures likewise do not define candidate-key minimality.
This concept has direct Data Quality implications. Correctly defined candidate and primary keys support uniqueness and integrity, prevent duplicate entity instances, enable reliable referential relationships, and improve entity matching within Master Data Management. Key definitions should also be captured as structural metadata so profiling and quality rules can consistently test duplicate and orphan conditions.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Keys; Candidate Keys; Chapter 13 - Uniqueness and Integrity; Metadata Management; Master Data Management.
NEW QUESTION # 76
What is one of the Data Architecture artifacts which are usually captured within development projects, and then standardised and managed by data architects?
Answer: A
Explanation:
The correct answer is Data models. DAMA-DMBOK2 explicitly explains that data models and other Data Architecture artifacts are commonly created or captured within development projects and are subsequently standardized and managed by Data Architects.
Development projects routinely produce conceptual, logical, and physical representations of the data required by a solution. If each project develops those structures independently without architectural review, inconsistent terminology, duplicated entities, conflicting definitions, and incompatible relationship structures can accumulate across the enterprise. Data Architects therefore review project models, reconcile them with enterprise standards, and identify structures suitable for reuse.
An Enterprise Data Model is itself an important architectural artifact, but the wording of the question refers specifically to artifacts typically generated in individual development projects and later standardized. The DMBOK2 passage uses data models in precisely that context.
This process has a direct Data Quality benefit. Standardized models improve consistency of definitions, domains, keys, relationships, and constraints. They also provide metadata from which integrity, uniqueness, validity, and completeness rules can be derived.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Architectural Artifacts; Development Projects; Chapter 5 - Data Modeling and Design; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 77
In data security, which of the following is not one of the four A's:
Answer: E
Explanation:
Agile is not one of the four A's of Data Security. DAMA security guidance groups core security processes around Access, Audit, Authentication, and Authorization. These capabilities collectively control who can interact with information resources, establish identity, determine permitted actions, and provide evidence of activity for monitoring and accountability. DAMA-aligned security references identify these four concepts directly.
Authentication confirms the identity of a user or system. Authorization determines what an authenticated identity is permitted to do. Access concerns the actual ability to obtain or interact with data and services.
Audit provides traceability by recording and reviewing relevant activities.
Agile, by contrast, is an approach to iterative delivery and project or product development. It may influence how security controls are implemented within development lifecycles, but it is not itself one of the foundational security-control categories identified in this model.
Data Security and Data Quality are separate but related disciplines. Unauthorized modifications can compromise accuracy and integrity, while weak auditability can prevent organizations from identifying how data was altered. Appropriate security controls therefore help preserve the reliability of governed information.
Metadata classification also supports these controls by identifying sensitive data and linking it to access and protection requirements.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Access; Authentication; Authorization; Audit; Data Governance; Chapter 13 - Integrity and Controlled Data Modification.
NEW QUESTION # 78
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