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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Topic 2: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Topic 3: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 4: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Topic 5: Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
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NEW QUESTION # 35
In 2009, ARMA International published GARP for managing records and information. GARP stands for:
Answer: B
Explanation:
GARP stands for Generally Accepted Recordkeeping Principles. ARMA International developed the framework to describe the characteristics of an effective records and information-management program. The principles address accountability, transparency, integrity, protection, compliance, availability, retention, and disposition.
These principles align closely with DAMA-DMBOK2's treatment of Document and Content Management.
Information must be created, organized, protected, maintained, retrieved, retained, and ultimately disposed of according to business, legal, regulatory, and historical requirements. Records management is therefore not simply about long-term storage. It establishes controls throughout the information lifecycle.
For example, the Integrity principle addresses the authenticity and reliability of records. Availability requires information to be retrievable accurately and efficiently. Retention requires organizations to keep records for justified periods, while Disposition governs appropriate final handling after those obligations expire.
These controls interact directly with Data Quality. Records that cannot be found, trusted, interpreted, or demonstrated to be authentic are not fit for their intended purpose even if they physically exist.
Metadata Management supplies classification, retention, provenance, ownership, and lifecycle metadata necessary to implement these principles consistently.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Records Management; GARP; Retention; Disposition; Integrity; Metadata Management.
NEW QUESTION # 36
A dataset contains every required customer record, but 8% of the telephone numbers belong to different people. Which statement is correct?
Answer: A
Explanation:
The dataset can be complete while remaining inaccurate. Completeness measures whether the required records and values are present; Accuracy measures whether those values correctly represent reality.
If every customer has a telephone number, the field may achieve 100% completeness. However, if 8% of those numbers belong to other people, the values are inaccurate.
This distinction is one of the most important principles in Data Quality measurement. A populated field is not automatically trustworthy. Similarly, a syntactically valid value is not necessarily accurate.
DAMA-aligned guidance explicitly distinguishes these dimensions and notes that a complete dataset may still contain incorrect values.
The appropriate quality scorecard should therefore present completeness and accuracy separately rather than treating one as evidence of the other.
For high-risk customer contact processes, the business may define independent thresholds for both dimensions. Verification services, customer confirmation, authoritative sources, and exception handling may be needed to improve accuracy after basic completeness has already been achieved.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Accuracy; Data Quality Dimensions; Metrics; Fitness for Purpose.
NEW QUESTION # 37
The requirement to enter a username, a password and then a code sent to an authentication app is called:
Answer: B
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 # 38
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: B
NEW QUESTION # 39
Project costs will increase faster by adding to the data model than adding to the process model because:
Answer: D
Explanation:
Each additional data item creates obligations across multiple lifecycle processes, which is why option D is the most complete answer. DAMA-DMBOK2 treats data as an asset that must be managed throughout an interconnected lifecycle rather than merely stored in a database. Activities include creation or acquisition, validation, integration, transformation, storage, maintenance, access, use, protection, retention, archiving, and disposal.
Consequently, introducing another meaningful data element may require changes to data models, metadata repositories, interfaces, mappings, business rules, validation controls, security classifications, quality measurements, lineage documentation, reporting logic, retention rules, and stewardship responsibilities. The cost is therefore cumulative across several management processes.
Option A identifies only migration. Option B reduces the impact to storage, which is usually a minor component. Option C applies only where a warehouse is involved. Option E identifies one legitimate consideration-security-but security is only one part of the complete management obligation.
The Data Quality implication is substantial. Every additional Critical Data Element may require explicit definitions of validity, completeness, accuracy, timeliness, and consistency, together with monitoring and issue-management procedures. This demonstrates why organizations should avoid collecting data indiscriminately and should manage data according to genuine business value and requirements.
Reference Topics: DAMA-DMBOK2 Chapter 1 - Data Lifecycle; Data as an Asset; Chapter 5 - Data Modeling; Chapter 13 - Data Quality Requirements and Monitoring.
NEW QUESTION # 40
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