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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Assessment and Measurement | 25% | - Metrics and KPIs - Benchmarking - Root Cause Analysis - Profiling Techniques |
| Topic 2: Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality - Data Quality in the Data Lifecycle |
| Topic 3: Data Quality Governance | 15% | - Policies and Procedures - Data Stewardship - Data Quality Roles and Responsibilities - Continuous Monitoring |
| Topic 4: Data Quality Improvement | 20% | - Data Cleansing Techniques - Standardization - Validation Rules - Data Enrichment |
| Topic 5: Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - Other Industry Standards - DAMA DMBOK Framework |
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NEW QUESTION # 52
A DMZ is bordered by 2 firewalls. These are between the DMZ and the:
Answer: E
Explanation:
A Demilitarized Zone (DMZ) is a controlled network segment positioned between an organization's trusted internal environment and an external, untrusted environment such as the Internet. In the classic architecture described in DAMA-DMBOK2, the DMZ is separated from the Internet by an external firewall and from internal organizational systems by another firewall. Therefore, the correct answer is Internet and internal systems.
DMBOK2 describes the DMZ as an area on the organization's perimeter, with firewall protection separating it from the organization and firewall protection between the DMZ and the Internet. It is commonly used for services or transient data exchanges that must interact with external parties without providing direct access to internal systems.
This arrangement implements defense in depth. Public-facing services can operate in the DMZ while internal databases and sensitive systems remain behind an additional security boundary. A compromise of a DMZ host should therefore not automatically expose the internal network.
For Data Quality, security architecture is relevant because unauthorized modification or corruption can compromise integrity and accuracy. Protecting interfaces and repositories supports trustworthy data processing as well as confidentiality and availability.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Network Security; Perimeter; DMZ; Firewalls; Integrity Protection.
NEW QUESTION # 53
Adoption of a Data Governance program is most likely to succeed:
Answer: A
Explanation:
DAMA-DMBOK2 recommends an incremental implementation strategy for Data Governance because governance introduces changes in accountability, decision rights, standards, processes, and organizational behaviour. Attempting an enterprise-wide implementation immediately creates unnecessary cultural and operational risk.
The implementation guidance is explicit that most governance rollout strategies are incremental, commonly beginning with a significant initiative such as Master Data Management or with a particular region, business unit, or domain. Enterprise-wide deployment as the initial step is described as uncommon.
Incremental implementation provides several control advantages. The organization can validate roles and decision mechanisms, establish stewardship practices, prove business value, refine policies, develop metrics, and correct weaknesses before expanding the governance framework. This aligns closely with Data Quality management, where organizations normally prioritize Critical Data Elements and high-value domains rather than attempting to measure and remediate every data element simultaneously.
Executive sponsorship is important, but a mandate alone does not produce sustainable adoption. Likewise, individual leadership charisma cannot substitute for operating processes, accountability, metrics, and change management. A short consultant-driven exercise also conflicts with DAMA's treatment of governance as an ongoing organizational capability.
Incremental rollout therefore offers the strongest mechanism for embedding governance permanently while demonstrating measurable value.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Implementation Guidelines; Organization and Culture; Adjustment and Communication; Chapter 13 - Data Quality Improvement and Governance.
NEW QUESTION # 54
In data modelling practice, entities are linked by:
Answer: D
Explanation:
In a data model, entities are linked by relationships. An entity represents a distinguishable business concept or thing about which the organization stores information, while a relationship expresses how one entity is associated with another.
For example, a Customer places an Order, an Order contains Order Lines, and a Product appears on an Order Line. Relationships therefore capture business semantics rather than merely physical implementation details.
Cardinality is an important characteristic of a relationship because it specifies how many instances of one entity may or must be associated with instances of another; however, cardinality is not itself the general mechanism by which entities are linked. DAMA-oriented modelling material identifies relationships as the correct linkage concept.
Indexes and triggers are physical database mechanisms. Processes describe business activities and may interact with entities, but they do not define entity-to-entity structure within a data model.
The Data Quality implications are significant. Properly defined relationships establish expectations for referential integrity. For example, an Order referencing a nonexistent Customer indicates an integrity defect.
Profiling can test orphaned foreign keys, invalid relationship cardinalities, and inconsistent associations.
Relationships and their cardinalities should therefore be documented as metadata and enforced through appropriate database, application, or quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Entities, Relationships and Cardinality; Logical Data Modeling; Chapter 13 - Integrity and Consistency; Metadata Management.
NEW QUESTION # 55
When a data quality team has more issues than they can manage, they should look to:
Answer: E
Explanation:
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
NEW QUESTION # 56
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
Answer: D
Explanation:
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
NEW QUESTION # 57
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