Chapter 1 Question Bank — Answer Key & Rationales
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Use this after attempting the questions. Each line gives the correct answer, the deciding rationale and the main reason the distractors fail.
1–10
C1-001 — A. Data Management is the broad coordinated discipline across plans, policies, programs, practices, value, protection and lifecycle. B is primarily Governance; C is analytics/reporting; D is database operations. Source: p. 19.
C1-002 — B. The primary business driver is obtaining value from data assets. Centralization and universal standardization are not universal goals, and business involvement is required. Source: p. 20.
C1-003 — C. Chapter 1 lists quality, privacy/confidentiality, protection, integrity, appropriate use and effective use, but not elimination of every duplicate regardless of purpose. Source: p. 20.
C1-004 — D. Data represents things other than itself and representational choices differ, so context and conventions are required. A–C invent restrictions not found in the source. Source: pp. 20–22.
C1-005 — A. The definition tells us what the value means and is therefore Metadata. B–D are business content/data instances. Source: pp. 21–24.
C1-006 — B. Chapter 1 rejects a rigid data→information ladder and treats the two as intertwined and often interchangeable. Source: p. 22.
C1-007 — C. Data is reusable and non-consuming; multiple users can use it simultaneously without using it up. A, B and D also occur with physical assets. Source: pp. 23–26.
C1-008 — D. No universal data-price standard exists, but organizations should use consistent methods that relate cost and benefit. A–C oversimplify or contradict the principle. Source: pp. 23, 26–27.
C1-009 — A. Fitness for stakeholder/business purpose is a core Data Quality concern. Security/availability/documentation do not substitute for trustworthy values. Source: pp. 23, 27–29.
C1-010 — B. Meaning, origin, movement and allowed use are Metadata/context problems. Source: pp. 23–24, 29–30.
11–20
C1-011 — C. Planning means considering downstream flows, quality, architecture and future use before/through implementation. The other choices are local, tool-first or reactive. Source: pp. 24, 28–29.
C1-012 — D. Data Management requires diverse technical and nontechnical skills across the lifecycle. A–C are unsupported extremes. Source: pp. 24–25, 29.
C1-013 — A. Conflicting local definitions of a shared concept require an enterprise perspective. Source: pp. 24–25, 29–30.
C1-014 — B. Different data types can have different lifecycle characteristics and management requirements. A, C and D contradict the chapter. Source: pp. 25, 30–32.
C1-015 — C. Data can create risk through loss, theft, misuse, misunderstanding, poor quality, privacy/ethical issues and information gaps. Risk does not replace Quality/Security. Source: pp. 25, 32–33.
C1-016 — D. Chapter 1 explicitly says Data Management requirements should drive IT decisions. Source: p. 25, p. 33.
C1-017 — A. Intangibility, copyability, reuse and theft without disappearance are characteristic challenges. The other choices describe the opposite. Source: pp. 25–26.
C1-018 — B. Replacement cost is one recognized valuation category. Row count/software price alone are insufficient; DAMA provides no universal price per GB. Source: pp. 26–27.
C1-019 — C. Rework/workarounds and lost productivity are hidden poor-quality costs. Source: pp. 27–28.
C1-020 — D. Chapter 1 encourages intentional data-product thinking and planning across business processes, technology, architecture and strategy. Source: pp. 28–29.
21–30
C1-021 — A. Chapter 1 observes that Metadata management can provide a useful starting point where shared definitions/context are weak. Metadata is itself data and serves many roles. Source: pp. 29–30.
C1-022 — B. Data moves horizontally across organizational verticals; local representations can conflict and require enterprise coordination. Source: pp. 29–30.
C1-023 — C. Section 2.5.8 requires consideration of internal/external data, future use, law/compliance and misuse risk. Source: p. 30.
C1-024 — D. Lineage traces a specific data set from origin through transformations to use. Lifecycle is broader. Source: pp. 30–31.
C1-025 — A. Data lifecycle follows the data asset; SDLC organizes development of systems/solutions. Source: pp. 30–32.
C1-026 — B. Data Security is managed across the lifecycle; Chapter 1 similarly treats Data Quality and Metadata Quality as cross-cutting. Source: pp. 31–32.
C1-027 — C. Focus on critical data and minimize ROT rather than attempt equal management of every item. Source: p. 32.
C1-028 — D. Different data types have different risks, roles and lifecycle requirements, so classification helps tailor controls. Source: p. 32.
C1-029 — A. An information gap is the difference between what is known and what is needed for effective decision-making. Source: p. 32.
C1-030 — B. Section 2.5.13 describes organizations that call data an asset but lack strategic knowledge, leadership commitment and cultural change. Source: pp. 33–34.
31–40
C1-031 — C. Data strategy should arise from business strategy and its inherent information/data needs. Source: p. 34.
C1-032 — D. Program Strategy is the management plan supporting the broader Data Strategy: quality, integrity, access, security, risk, roles, initiatives and execution. Source: p. 34.
C1-033 — A. Vision, business case, goals, principles, measures, risks and operating model describe the Charter. Source: pp. 34–35.
C1-034 — B. Planning-horizon goals plus accountable roles/organizations/leaders describe the Scope Statement. Source: p. 35.
C1-035 — C. Programs, projects, task assignments and milestones are the Implementation Roadmap. Source: p. 35.
C1-036 — D. Multiple frameworks provide different levels of abstraction for alignment, strategy, dependencies, organization and application; they are complementary lenses. Source: pp. 35–36.
C1-037 — A. Those four named domains identify SAM. Source: p. 36.
C1-038 — B. AIM is the 9-cell Business/Information/IT × Strategy/Tactics/Operations model. Source: pp. 36–37.
C1-039 — C. The DAMA Wheel maps the 11 Knowledge Areas and places Governance centrally. Source: pp. 37–38.
C1-040 — D. The Environmental Factors Hexagon places Goals & Principles at the center. Governance is central in the Wheel, not the Hexagon. Source: p. 38.
41–53
C1-041 — A. Those components define a Knowledge Area Context Diagram. Source: pp. 39–41.
C1-042 — B. Control assures ongoing quality, integrity, reliability and security. Plan sets direction; Develop is build/test/deploy; Operate is ongoing support/use. Source: p. 40.
C1-043 — C. Participants perform, manage the performance of or approve activities. Suppliers provide inputs; Consumers benefit from outputs. Source: pp. 40–41.
C1-044 — D. Deliverables are tangible activity outputs and may become inputs elsewhere. Source: p. 40.
C1-045 — A. Aiken is a progression/dependency lens, but the DMBOK explicitly notes real organizations may establish capabilities in arbitrary or imperfect order. Source: pp. 41–42.
C1-046 — B. Functional Area Dependencies emphasizes BI/Analytics' reliance on upstream systems, Quality, Design, Integration, Master Data, warehouse and governance-related foundations. Source: pp. 42–43.
C1-047 — C. The Function Framework lists data risk management (security/privacy/compliance), Metadata and Data Quality as foundational activities. Source: pp. 43–45.
C1-048 — D. DMBOK provides common vocabulary, a functional framework, widely adopted practices and a fundamental CDMP reference. Source: pp. 45–47.
C1-049 — A. Knowledge Area chapters include Introduction, Activities, Tools, Techniques, Implementation Guidelines, Relation to Data Governance and Metrics. Source: p. 47.
C1-050 — B. Data Integration & Interoperability centers on movement/consolidation within and between stores, applications and organizations. Source: p. 47.
C1-051 — C. Metadata Management supplies definitions, models, flows and other context needed to understand data and systems. Source: p. 48.
C1-052 — D. Data Quality measures, assesses and improves fitness for use. Source: p. 48.
C1-053 — A. Governance is central because it supports consistency within and balance between Data Management functions; it does not replace or perform every other Knowledge Area. Source: pp. 37–38.
Correct-answer distribution
A = 14 · B = 13 · C = 13 · D = 13