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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
| Topic 2: Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
| Topic 3: Data Quality Concepts and Principles | 15% | - Data quality dimensions - Definition and importance of data quality - Data quality in DMBOK framework |
| Topic 4: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Topic 5: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
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NEW QUESTION # 59
An organization changes the definition of "Net Revenue". Before implementing the change, it wants to identify every report, transformation, and analytical model that depends on the existing definition. Which capability is most important?
Answer: D
Explanation:
The required capability is Metadata lineage and impact analysis. Changing a business definition can affect far more than the glossary entry itself. The organization needs to know which physical fields, transformation rules, ETL jobs, calculations, reports, dashboards, analytical models, and downstream datasets depend on the current definition.
Lineage shows where data originates and how it flows and transforms. Impact analysis follows those relationships forward to determine what will be affected by a proposed change.
This prevents a common Data Quality failure in which a definition changes in one area while downstream systems continue applying the old interpretation. The result can be technically correct calculations that are semantically inconsistent.
A governed change should therefore update the business glossary, transformation metadata, Data Quality rules, reporting definitions, and affected documentation in a coordinated manner. Appropriate Data Stewards and Data Owners should approve the new meaning and effective date.
DAMA's framework describes Metadata Management as enabling understanding through definitions, lineage, and usage across systems, while the revised Chapter 13 explicitly strengthens the Data Quality-Metadata relationship.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Impact Analysis; Business Glossary; Data Quality Consistency.
NEW QUESTION # 60
The process of translating plain text into complex codes to hide privileged information is:
Answer: B
Explanation:
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.
NEW QUESTION # 61
Examples of transformation include:
Answer: E
Explanation:
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
NEW QUESTION # 62
The requirement to enter a username, a password and then a code sent to an authentication app is called:
Answer: E
Explanation:
This is two-factor authentication (2FA). Authentication factors are distinguished by the type of evidence used to verify identity. A username identifies the account but does not constitute a separate authentication factor.
The password represents something the user knows, while the code generated or delivered through an authentication application represents something the user has. Because two different factor categories are involved, the mechanism is two-factor authentication.
DAMA-DMBOK2 discusses multiple-factor identification as a security control for systems containing sensitive information. Its examples include a code returned to a user's mobile device, possession of a hardware device, and biometric factors such as fingerprints or facial recognition. DMBOK2 specifically notes that two-factor identification makes unauthorized account access substantially more difficult.
Three-factor authentication would require a third independent category, typically something the user is, such as a biometric characteristic. The mere fact that a username, password, and code are entered does not create three factors because the username is an identifier rather than authentication evidence.
From a Data Quality perspective, strong authentication protects integrity by reducing the risk that unauthorized users alter governed data, quality rules, metadata, or master records.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authentication; Multiple-Factor Identification; Authorization; Data Security; Data Integrity.
NEW QUESTION # 63
One way of defining ethics is:
Answer: A
Explanation:
The correct principle is "Doing it right when no one is looking." The statement captures the essential distinction between ethical behaviour and mere compliance. Ethical conduct does not depend on observation, enforcement, or the probability of being caught; it requires responsible action because the action itself is appropriate. The answer wording is presented directly in the PDF on page 4.
DAMA-DMBOK2 treats data ethics as an essential component of professional Data Management because organizations routinely make choices about collecting, combining, analyzing, retaining, sharing, and monetizing data. A technically permissible activity may still create inappropriate effects on individuals or expose data to potential misuse.
Data ethics therefore extends beyond security and regulatory compliance. It requires consideration of the impact on people, potential for misuse, and economic value of data. Ethical handling also requires transparency concerning data provenance, intended use, quality limitations, and the consequences of decisions based on data.
Data Quality has an ethical dimension as well. Knowingly using inaccurate, incomplete, misleading, or poorly understood data in consequential decisions can harm customers and other stakeholders. Governance should therefore establish not only compliance controls but also principles for responsible use.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Ethical Principles; Impact on People; Potential for Misuse; Chapter 13 - Reliable and Fit-for-Purpose Data.
NEW QUESTION # 64
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