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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Assessment methods and tools - Data profiling techniques |
| Topic 2: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality tools and technologies - Data quality in lifecycle management |
| Topic 3: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Topic 4: Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Topic 5: Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
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NEW QUESTION # 90
The Data Architecture workstream on results produces:
Answer: D
Explanation:
Within DAMA-DMBOK2, establishing a Data Architecture practice is divided into several complementary workstreams. The Results workstream is specifically responsible for producing Data Architecture artifacts within an overall roadmap. The other answer choices correspond to different aspects of the architecture practice rather than to the Results workstream itself.
DMBOK2 distinguishes the workstreams clearly: Strategy selects frameworks and develops the roadmap; Acceptance and Culture addresses awareness and behavioural change; Organization assigns architecture accountability and responsibility; Working Methods establish practices and coordinate architecture work with development initiatives; and Results produces the architectural artifacts that collectively realize the roadmap.
Typical artifacts include enterprise data models, high-level data-flow designs, architectural standards, transition-state specifications, and related metadata. These artifacts represent both current and target architectural states and allow individual projects to align with enterprise direction.
The Data Quality connection is substantial because architectural artifacts identify authoritative sources, integration points, critical data flows, entities, relationships, and dependencies. Those structures provide the context needed to establish validity, integrity, consistency, lineage, and quality-monitoring requirements.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Establish Data Architecture Practice; Results Workstream; Data Architecture Artifacts; Architecture Roadmap; Chapter 13 - Data Quality Dependencies.
NEW QUESTION # 91
The process of translating plain text into complex codes to hide privileged information is:
Answer: A
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 # 92
A security mechanism that searches for customer bank account details in outgoing emails is achieving the goal of:
Answer: A
Explanation:
The mechanism is designed to meet confidentiality and privacy requirements. Customer bank-account details constitute sensitive financial information. Inspecting outgoing email for such values is a form of Data Loss Prevention control intended to identify or prevent inappropriate disclosure before sensitive information leaves the organization's controlled environment.
DAMA-DMBOK2 treats confidentiality and privacy as fundamental Data Security requirements. Its electronic-communication guidance warns that restricted or confidential information should not be sent through insecure communication channels because messages can be intercepted, forwarded, or otherwise disclosed after leaving the sender's control. DAMA International's own privacy practices likewise classify bank-account and similar financial information as protected personal/financial data requiring security safeguards against unauthorized disclosure.
The other responses concern transparency, definitions, user experience, or availability and do not address the security objective demonstrated in the scenario.
Data Governance determines the classifications and policies governing such information; Metadata Management can record sensitivity classifications against physical data elements; and security technology then enforces the resulting controls.
Data Quality remains relevant because confidentiality controls must distinguish genuinely sensitive values accurately. Poor classification or inaccurate detection rules may either expose protected information or unnecessarily block legitimate communication.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Confidentiality and Privacy; Electronic Communication Security; Sensitive Data; Data Loss Prevention; Data Governance.
NEW QUESTION # 93
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: B
Explanation:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
NEW QUESTION # 94
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: C
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 95
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