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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Data Quality Assessment and Measurement | 25% | - Metrics and KPIs - Profiling Techniques - Benchmarking - Root Cause Analysis |
| Data Quality Governance | 15% | - Policies and Procedures - Continuous Monitoring - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - Other Industry Standards - DAMA DMBOK Framework |
| Data Quality Improvement | 20% | - Data Enrichment - Standardization - Data Cleansing Techniques - Validation Rules |
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NEW QUESTION # 78
A Data Quality team repeatedly corrects invalid postal codes in the warehouse, but the same errors reappear after every nightly load. What is the most appropriate long-term response?
Answer: D
Explanation:
The correct long-term response is to remove the root cause in the source or integration process. Repeated warehouse cleansing treats symptoms. If the defect is recreated every night, operational costs continue and downstream systems remain exposed until the cleansing process executes.
DAMA Data Quality practice emphasizes sustained improvement rather than repeated correction. The quality lifecycle therefore includes identifying defects, determining business impact, analyzing root causes, implementing corrective actions, and monitoring results. The current Chapter 13 revision specifically strengthens clarification of the Data Quality Improvement Lifecycle and responsibilities within it.
The underlying cause might be weak source validation, an incorrect source-to-target mapping, outdated Reference Data, transformation logic, or missing governance over postal-code rules.
Cleansing remains appropriate where historical data must be repaired or immediate downstream protection is required. However, preventive control should be introduced as close to the creation point as practical.
Metadata and lineage help trace the postal code through the data flow, while governance establishes who has authority to change the offending process.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Remediation; Prevention; Cleansing; Data Integration and Lineage.
NEW QUESTION # 79
A minimal super key is:
Answer: D
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 # 80
Emergency contact phone number would be found in which master data management program?
Answer: C
Explanation:
An emergency contact phone number belongs to the Employee master-data domain. Employee Master Data describes relatively stable information about employees that needs to be consistently identified and reused across organizational processes and systems. Contact details associated with an employee-including emergency-contact information-therefore fall naturally within an Employee MDM program rather than Product or Asset MDM.
The broader DAMA Master Data framework identifies employees as one of the recurring enterprise entities that may require coordinated management alongside customers, products, suppliers, locations, and similar core subjects. Published versions of this specific DAMA certification item likewise identify Employee as the correct domain.
Managing this information centrally or through coordinated master-data processes helps prevent conflicting phone numbers, obsolete emergency contacts, duplicate employee identities, and inconsistent values across Human Resources and other authorized systems.
The data is also sensitive personal information. Governance should therefore define ownership, access rights, retention rules, and permitted usage. Quality controls should address completeness, validity, currency, and accuracy, because outdated emergency-contact information may fail precisely when it is needed.
Metadata Management should record definitions and classifications so that "emergency contact phone number" is consistently distinguished from an employee's own mobile or work telephone number.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Domains; Employee Master Data; Data Governance; Chapter 13 - Accuracy, Completeness and Currency.
NEW QUESTION # 81
Compound authorization groups provide a means to:
Answer: C
Explanation:
Compound authorization groups allow access rights from multiple groups or roles to be combined so that an individual's effective permissions can be configured with greater precision. The user may inherit one set of privileges from an organizational role, another from a functional group, and additional permissions from a specialized operational responsibility. The resulting authorization can therefore be more granular than assigning every privilege individually.
DAMA's Data Security framework separates authentication-establishing identity-from authorization, which determines what an authenticated identity is permitted to access or perform. Group-based authorization is a practical mechanism for implementing those decisions. CDMP-oriented references for this specific item identify "precisely configure an individual's access to a system" as the intended answer.
The principle must nevertheless be governed carefully. Excessive overlapping group memberships can make effective permissions difficult to review and can create segregation-of-duties conflicts. Periodic access reviews, least-privilege design, role ownership, and audit trails are therefore necessary complementary controls.
Encryption addresses confidentiality of data in transit or storage and is unrelated to how permissions are assigned. Audit preparation may benefit from structured authorization, but it is not the principal purpose.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authorization; Access Control; Least Privilege; Security Roles; Audit and Monitoring.
NEW QUESTION # 82
Data governance represents:
Answer: C
Explanation:
DAMA-DMBOK2 explicitly states that Data Governance represents an inherent separation of duty between oversight and execution. It illustrates the principle through an analogy with financial governance: an auditor exercises oversight over financial processes without personally executing financial management. Similarly, Data Governance ensures that data is properly managed but does not itself perform every operational Data Management activity.
This separation is fundamental because governance must retain sufficient independence to establish rules, monitor compliance, resolve conflicts, assign accountability, and evaluate whether operational teams are managing data according to approved policies and standards.
Option B describes activities in which governance and Data Quality practitioners may collaborate, but it is not the defining governance principle. Option D is also incorrect because federated governance is only one possible operating model; DMBOK2 also recognizes centralized and replicated approaches. Option E describes an organizational implementation, not the conceptual distinction.
Within Data Quality Management, this means that governance may approve quality policies, Critical Data Elements, acceptable thresholds, stewardship structures, and escalation procedures, while DQ analysts and operational teams perform profiling, cleansing, monitoring, and remediation.
Maintaining this division reduces conflicts of interest and establishes accountability for whether data- management activities achieve agreed business objectives.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Essential Concepts; Oversight versus Execution; Governance Operating Models; Chapter 13 - Data Quality Governance and Operational Responsibilities.
NEW QUESTION # 83
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