Free DAMA CDMP Fundamentals Practice Questions: Data Governance

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Sample questions

These are original IT Mastery practice questions aligned to this topic area. They are not official exam questions, copied live-exam content, or exam dumps. Use them for self-assessment, scope review, and deciding what to drill next.

Question 1

Topic: Data Governance

A bank is launching a governed data set for customer profitability reporting. Finance and product teams disagree on the business definition of active customer, and the reporting team needs an approved definition and quality expectations before publishing dashboards. Which role should be accountable for approving the definition and expectations?

Options:

• A. Subject-matter expert

• B. Data custodian

• C. Data steward

• D. Data owner

Best answer: D

Explanation: In data governance, the data owner has business accountability for a data asset. For a governed data set, that includes approving business definitions, resolving business-level conflicts, and setting expectations for acceptable data quality. Data stewards often prepare definitions, coordinate reviews, maintain glossary entries, and monitor issues, but they usually do not carry final accountability unless explicitly delegated. Custodians manage technical environments and operational handling of data. Subject-matter experts provide domain knowledge, but their input supports the accountable decision maker rather than replacing that accountability. The key distinction is accountability versus support or execution.

• Custodian confusion fails because technical management of storage, access, or processing does not make the custodian accountable for business definitions.
• Steward overreach fails because stewardship commonly facilitates and documents governance decisions rather than owning final business accountability.
• SME input fails because expertise informs the decision, but the SME is not automatically accountable for approving governed data standards.

Question 2

Topic: Data Governance

A regional insurer is launching a customer data hub to support claims, underwriting, and compliance reporting. The same customer identifier is currently defined differently by two business units, integration work is already underway, and regulators require consistent reporting lineage. Which action is the best professional decision for assigning decision rights?

Options:

• A. Let the project manager decide the definition to keep the integration schedule on track

• B. Ask database administrators to choose the identifier format that is easiest to index

• C. Have the data governance council assign a business data owner to approve the identifier definition and stewardship rules

• D. Have data entry supervisors standardize the identifier during daily operational processing

Best answer: C

Explanation: Governance decision rights define who is authorized to make business decisions about shared data, especially definitions, policies, ownership, and stewardship. In this situation, the customer identifier affects multiple business units and regulatory reporting lineage, so the decision cannot be treated as a local technical, schedule, or operational preference. A governance body should ensure that the right business data owner has authority to approve the definition and related stewardship rules. IT administrators may implement the approved standard, project managers may coordinate delivery, and operational teams may follow procedures, but they should not own cross-enterprise meaning and accountability for shared data.

• Technical convenience fails because indexing efficiency does not determine the approved business meaning of a shared identifier.
• Schedule pressure fails because project management coordinates work but should not override enterprise data accountability.
• Operational cleanup fails because data entry teams can apply standards but should not define cross-business governance rules.

Question 3

Topic: Data Governance

Sales and Finance use different definitions of Active Customer in enterprise reporting. The assigned data stewards documented the conflict, reviewed existing glossary entries, and could not reach agreement. The difference affects customer profitability metrics used by both domains and executive reporting. What is the best governance response?

Options:

• A. Allow each domain to keep its own definition in the glossary

• B. Escalate the issue to the data governance council for a cross-domain decision

• C. Ask the database administrator to standardize the reporting tables

• D. Assign the report developer to choose the most used definition

Best answer: B

Explanation: Governance escalation is appropriate when a data issue crosses business domains, affects important business decisions, and cannot be resolved by normal stewardship work. The stewards have already documented the issue and attempted resolution, so the next step is not a technical change or local workaround. A governance council or equivalent decision body can resolve the policy-level conflict, assign accountability, approve the authoritative definition, and ensure the decision is recorded in governance artifacts such as the business glossary and issue log. The key distinction is between stewardship facilitation and formal governance decision rights.

• Technical standardization fails because the conflict is semantic and accountable-business-driven, not primarily a database structure problem.
• Separate definitions fails because it leaves an enterprise reporting conflict unresolved and weakens shared understanding.
• Developer selection fails because report developers do not own cross-domain business definitions or governance decisions.

Question 4

Topic: Data Governance

A data governance council is launching a customer analytics initiative. Sales, finance, and support use different meanings for terms such as “active customer” and “customer start date.” The council needs an approved, business-facing artifact that records standard term names, definitions, and accountable stewards. Which artifact best fits this need?

Options:

• A. Physical data model

• B. Data retention schedule

• C. Data issue log

• D. Business glossary

Best answer: D

Explanation: A business glossary supports governance by standardizing business vocabulary across domains. It records approved terms, agreed definitions, synonyms or naming conventions when needed, and stewardship responsibilities. In this scenario, the main problem is semantic inconsistency: different business areas use the same customer terms in different ways. A governed glossary gives reporting and analytics teams a common reference before data models, metrics, or reports are built.

A retention schedule, physical model, or issue log may support governance in other situations, but they do not primarily establish shared business meaning.

• Retention schedule fits legal or policy-driven rules for how long data is kept, not term definition conflicts.
• Physical data model describes database structures and implementation details, not the approved business vocabulary.
• Issue log tracks defects, decisions, and remediation work, but it is not the authoritative source for standard definitions.

Question 5

Topic: Data Governance

A company has an approved data classification policy and a privacy policy for customer data. However, it has no standard requiring BI teams to document source-to-report lineage or transformation rules for published metrics. Finance and marketing now publish different customer churn figures and cannot trace how either value was derived. Which risk does this policy gap most directly create?

Options:

• A. Duplicate customer master records

• B. Premature deletion of customer records

• C. Unreliable reporting with weak auditability

• D. Unauthorized access to classified data

Best answer: C

Explanation: A governance policy gap should be interpreted by the control it fails to provide. Here, classification and privacy rules exist, but there is no standard for documenting lineage and transformations from source data to BI metrics. That creates a direct risk to data quality, reporting trust, and compliance evidence because stakeholders cannot determine which source, rule, or calculation produced a published figure. The issue is not primarily access control, retention, or master data matching. The key risk is that business decisions and reviews rely on metrics that cannot be traced or consistently validated.

• Access control is not the main gap because the scenario says classification and privacy policies already exist.
• Retention failure is unsupported because no deletion schedule or records-retention rule is described.
• Master data duplication could affect churn, but the visible gap is undocumented metric lineage and transformation logic.

Question 6

Topic: Data Governance

A data governance council has completed its first year and wants to show whether governance practices are producing measurable value, not just that meetings occurred. Which set of metrics best demonstrates data governance effectiveness?

Options:

• A. Project budget variance, sprint velocity, help desk tickets, and software release frequency

• B. Dashboard color scheme, report page views, user interface defects, and refresh duration

• C. Policy adoption, issue resolution, stewardship participation, quality improvement, and business impact

• D. Number of databases, storage growth, backup success rate, and server uptime

Best answer: C

Explanation: Data governance effectiveness should be measured with indicators that show whether governance decisions are being adopted and producing better outcomes. Useful measures include policy and standard adoption, timely resolution of data issues, active participation by stewards and owners, measurable improvement in data quality, and evidence of business impact such as reduced risk, faster reporting, or improved decision making. These metrics connect governance work to behavior change and organizational value. Purely technical operations, dashboard usage, or project delivery measures may be useful in other contexts, but they do not directly show whether governance is working.

• Technical operations such as uptime and backup success measure platform reliability, not governance adoption or value.
• Dashboard presentation measures focus on BI consumption and usability, not whether governance controls are effective.
• Project delivery measures such as sprint velocity and release frequency show execution performance, not data governance outcomes.

Question 7

Topic: Data Governance

A finance team needs trusted customer revenue data for a regulatory report due in 6 weeks. Recurring defects come from the CRM-to-warehouse feed, and IT says fixes are blocked until business rules are confirmed. The new governance forum reviews the issue log below. Which governance weakness is most preventing timely resolution and accountability?

Options:

• A. No automated data profiling tool for CRM records

• B. No accountable owner or steward with escalation authority

• C. No standard technical mapping template for the feed

• D. No redesigned dashboard for regulatory reporting

Best answer: B

Explanation: Issue management in data governance requires clear accountability for decisions, ownership of remediation, target dates, and escalation when issues stall. In this situation, the defects are known and recurring, but the log uses “TBD,” shared technical teams, and no escalation record. Because IT is blocked by unresolved business rules, the main weakness is not detection or implementation format. The governance forum needs to assign the issue to an accountable data owner or steward with decision rights and an escalation route so definitions, hierarchy rules, and exception handling can be resolved before the reporting deadline. Technical fixes can follow once governance decisions are made.

• Profiling focus may help discover defects, but the visible problem is stalled decision making after defects are already known.
• Mapping template could improve implementation consistency, but it cannot decide business definitions or ownership.
• Dashboard redesign affects presentation, not the unresolved data-rule accountability causing the reporting risk.

Question 8

Topic: Data Governance

Sales and Finance use different definitions for “active customer,” causing inconsistent executive reports. The CRM team maintains the application, but no business group has authority to approve a shared definition or resolve future disputes. Which governance mechanism best addresses the issue?

Options:

• A. A new enterprise data warehouse schema

• B. A data profiling and cleansing initiative

• C. A data governance council with assigned data owners

• D. A database administration standard

Best answer: C

Explanation: Conflicting business definitions and unclear ownership are governance operating model issues. DAMA-aligned data governance establishes decision rights, accountabilities, and escalation paths so business stakeholders can approve common definitions and consistently apply them across reports, systems, and projects. Data owners are accountable for business meaning and policy decisions; stewards help define, document, and apply those decisions. Technical teams may implement the decision, but they should not be the default authority for business semantics. The key distinction is between governing the decision and implementing a technical fix.

• Database administration focuses on technical operation and standards, not authority for business definitions.
• Data profiling can reveal differences or defects, but it does not assign ownership or approve a shared meaning.
• Warehouse redesign may standardize reporting structures, but it cannot resolve decision rights by itself.

Question 9

Topic: Data Governance

A company is creating an enterprise customer KPI. Sales defines active customer as any account with an open opportunity; Finance defines it as an account with an invoice in the last 12 months. The CRM team can implement either definition, and the project manager wants a decision this week. Who should have the decision right to approve the enterprise definition?

Options:

• A. Business analyst documenting CRM requirements

• B. Data governance council or accountable data owner

• C. Database administrator for the CRM platform

• D. Project manager for KPI delivery

Best answer: B

Explanation: Governance decision rights define who is authorized and accountable for decisions about shared data meaning, policies, standards, and issue resolution. An enterprise KPI definition affects multiple business functions, so the decision should sit with the appropriate governance body or accountable data owner. IT administration can implement the approved definition, project management can coordinate timing, and business analysis can document requirements and impacts. Operational staff may enter or maintain records, but they do not normally approve enterprise data definitions. The key distinction is accountability for data meaning versus responsibility for delivery, configuration, analysis, or entry.

• System administration can configure CRM fields and controls, but it does not set an enterprise business definition.
• Project management coordinates schedule and delivery, but deadline pressure does not create authority over shared data meaning.
• Business analysis captures and analyzes requirements, but unresolved cross-functional definitions need formal governance approval.

Question 10

Topic: Data Governance

A data governance council is responding to inconsistent handling of customer data across business units. The council wants an approved enterprise artifact that states management intent, accountability, and mandatory principles for customer data use. It should be stable across technologies and should not prescribe field formats, workflow steps, or tool configuration. Which artifact best fits this need?

Options:

• A. Customer data quality control

• B. Customer data standard

• C. Customer data stewardship procedure

• D. Customer data policy

Best answer: D

Explanation: In data governance, a policy is the high-level authoritative statement of intent, principles, and accountability for managing data. The scenario asks for an enterprise-level artifact that is approved by the governance council, applies across business units, and remains stable regardless of technology. That points to a policy. Standards translate policy into specific mandatory requirements, such as naming conventions, code values, or required metadata fields. Procedures describe repeatable steps for carrying out work. Controls are mechanisms used to verify, prevent, or detect noncompliance. The key takeaway is that policy defines what the organization requires and why, while the other artifacts define how requirements are specified, performed, or checked.

• Standard detail fails because field formats and naming conventions are more specific than the requested enterprise intent.
• Quality control fails because a control checks or enforces compliance rather than stating governing principles.
• Stewardship procedure fails because workflow steps are explicitly outside the requested artifact.

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