DAMA Updated DQ-1220 Exam Questions and Answers by husna

The process is Encryption. DAMA-DMBOK2 defines encryption as converting readable plaintext into coded information so that privileged or sensitive content cannot be understood without the appropriate decryption mechanism. The DMBOK2 security section specifically describes encryption as translating plain text into complex codes to hide privileged information and also notes its use in protecting transmission integrity and validating identity.

Encryption supports confidentiality both at rest and in transit. Depending on implementation, organizations may use symmetric encryption, asymmetric or public-key encryption, hashing for particular integrity or authentication functions, and other cryptographic mechanisms.

The crucial distinction is that encryption deliberately changes the representation of information according to a cryptographic algorithm and key structure. Encapsulation, enhancement, elimination, and exaggeration are not substitutes for cryptographic protection.

Within the wider DAMA framework, encryption should be applied according to classification and risk. Highly sensitive information may require stronger cryptographic controls, restricted key management, rotation procedures, and separation of duties.

Data Quality and Data Security intersect at integrity: unauthorized changes may produce inaccurate data even when availability remains unaffected. Security controls therefore protect not merely secrecy but the reliability and trustworthiness of managed information.

Reference Topics: DAMA-DMBOK2 Chapter 7 — Encryption; Confidentiality; Data Integrity; Cryptographic Controls; Security Classification.

===============

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.

===============

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.

===============

DAMA-DMBOK2 states that Data Modeling tools and model repositories are necessary for managing the Enterprise Data Model at all levels. This includes maintaining relationships among conceptual, logical, and physical representations and controlling how models created for different purposes fit into the wider enterprise architecture.

An enterprise modeling repository provides more than diagramming. It supports version control, definitions, lineage between model layers, comparison of changes, reuse of standard entities and attributes, enforcement of modelling conventions, and communication across projects. Many modelling tools also provide relationship and lineage capabilities that enable architects to trace how concepts represented at a high level are implemented in detailed models.

Option C describes one important capability—visualizing and communicating database designs—but it is narrower than the DMBOK2 purpose stated in the question. Similarly, modelling tools support governance and architectural visualization but are not solely established for either function.

For Data Quality, governed models provide the structural metadata from which rules can be derived. Keys support uniqueness; relationships support integrity; domains support validity; mandatory attributes support completeness; and standardized definitions support consistency.

The repository therefore becomes an important bridge between Data Architecture, Metadata Management, Data Governance, and Data Quality.

Reference Topics: DAMA-DMBOK2 Chapter 5 — Data Modeling Tools and Repositories; Enterprise Data Model; Conceptual/Logical/Physical Models; Chapter 13 — Structural Quality Rules.

===============