Free DAMA CDMP Governance Practice Questions: Data Quality Governance

Topic snapshot

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 Quality Governance

A bank’s customer data team runs a monthly cleanup that merges duplicate customer records. The duplicate rate improves for two weeks, then returns to the same level. Project teams disagree about which system may create new customer identifiers, and no forum has resolved the conflict. What governance gap is most likely preventing long-term improvement?

Options:

• A. Insufficient data profiling frequency

• B. Unclear decision rights for root-cause remediation

• C. Lack of a more advanced matching tool

• D. Incomplete monthly cleanup procedures

Best answer: B

Explanation: Recurring data quality problems usually indicate a governance accountability gap when cleanup temporarily improves results but the same defects return. In this case, the unresolved disagreement about which system may create customer identifiers points to unclear decision rights and ownership for root-cause remediation. Data governance should assign accountable data owners or stewards, define creation rules, agree controls, and use an escalation forum when business areas cannot resolve the conflict. Profiling and matching tools can detect or repair duplicates, but they do not decide who has authority to change upstream behavior.

• Profiling frequency may improve visibility, but the scenario already shows the defect pattern and unresolved ownership conflict.
• Advanced matching may make cleanup more efficient, but it does not prevent systems from creating duplicates.
• Cleanup procedures address the symptom after records exist, not the governance decision needed to stop recurrence.

Question 2

Topic: Data Quality Governance

A finance transformation program is preparing monthly revenue quality scorecards for executive reporting and controls testing. Sales and Finance disagree about whether cancelled orders should count in recognized revenue. Audit requires each enforced rule to have accountable remediation and an approved tolerance.

Which governance decision BEST addresses the weakness affecting trust and enforceability?

Options:

• A. Escalate revenue-rule ownership and threshold approval to the governance council

• B. Ask the ETL lead to remediate all invoice ID failures

• C. Lower the invoice ID threshold until the scorecard passes

• D. Publish the scorecard with a note about the Sales-Finance dispute

Best answer: A

Explanation: A quality rule is enforceable only when its business meaning, accountable owner, threshold, and escalation path are governed. The invoice ID rule is being treated as a project or technical target even though it affects executive reporting and controls testing. Because Sales and Finance disagree about the business definition, the issue should be escalated through governance so decision rights are clear and an accountable business owner approves the rule and tolerance. Technical remediation can follow, but it should not substitute for governance approval of the rule.

• Technical cleanup fails because the ETL lead may fix defects but should not own the business threshold or definition.
• Lowering the threshold weakens trust because it changes tolerance without accountable approval.
• Publishing with a caveat leaves the rule disputed, making the scorecard difficult to rely on or enforce.

Question 3

Topic: Data Quality Governance

A customer master data quality issue is causing rejected invoices. Sales argues Finance should fund remediation because Finance is affected; Finance argues the CRM team should fund it because the error originates in CRM. IT can implement changes but will not proceed without business approval.

Governance charter excerpt:

• Customer Data Owner: accountable for customer master quality and remediation prioritization
• Data Steward: coordinates issue workflow and validates rules
• System Custodian: implements approved technical changes
• Data Governance Council: resolves cross-domain accountability disputes

What should governance do next?

Options:

• A. Let the CRM project team decide based on release capacity

• B. Have the council apply the charter and assign accountability to the Customer Data Owner

• C. Require IT to fund and perform remediation because CRM stores the data

• D. Require Finance to fund remediation because rejected invoices create the impact

Best answer: B

Explanation: Data quality remediation accountability should follow defined governance decision rights, not the department experiencing the pain or the system team holding the data. The charter separates accountability from execution: the Customer Data Owner is accountable for customer master quality and prioritization, the Data Steward coordinates the issue workflow and rule validation, and the System Custodian implements approved technical changes. Because Sales, Finance, and IT disagree, the Data Governance Council should resolve the dispute by applying the charter and escalation path. The key distinction is business accountability for data quality versus technical custody of the platform.

• Impact-based funding fails because being harmed by poor data does not automatically make Finance accountable for customer master remediation.
• Technical custody fails because IT may implement fixes, but storing data in CRM does not make IT the business owner of data quality.
• Project convenience fails because release capacity is an implementation constraint, not the governance basis for deciding accountability.

Question 4

Topic: Data Quality Governance

A data governance team is reviewing a customer data quality rule before using it in enterprise scorecards.

Which governance weakness most directly affects trust and enforceability?

Options:

• A. The threshold is less than a 100% pass rate.

• B. The scorecard has not yet been automated.

• C. The ETL team proposed the rule logic.

• D. No accountable business owner has approved the rule and threshold.

Best answer: D

Explanation: Quality rules need business accountability, not only technical implementation. The summary shows a disputed business term, a threshold set by a technical role, no accountable owner, and no approval by a data owner or governance forum. That means stakeholders can challenge whether the rule reflects the intended business meaning and whether the 95% threshold is acceptable. Data stewards and technical teams may help define, measure, and implement rules, but decision rights for business meaning and acceptable quality levels must be clear. Automation can improve monitoring later, but it does not make an unapproved rule trusted or enforceable.

• Perfect quality target is not automatically required; thresholds should reflect approved business tolerance and risk.
• Technical proposal can be useful input, but it does not replace business decision rights.
• Scorecard automation helps reporting efficiency, but it cannot solve an unresolved ownership and approval gap.

Question 5

Topic: Data Quality Governance

A data governance council reviews a customer data quality scorecard. The overall score has improved from 82% to 96% after duplicate records were reduced. Sales leaders still do not trust the monthly account pipeline report because account ownership is often outdated, causing opportunities to be assigned to the wrong territory. What is the best governance response?

Options:

• A. Create a one-time cleanup of territory assignments

• B. Ask IT to refresh the report more frequently

• C. Revise the scorecard to include ownership currency

• D. Continue reporting the improved duplicate-rate score

Best answer: C

Explanation: A data quality scorecard should measure dimensions that matter to business use, not only dimensions that are easy to improve. In this case, duplicate reduction improved a measured score, but the business distrusts the report because account ownership is not current. The governance response is to align the scorecard with the critical data requirement for the report: current and valid ownership for territory assignment. That may lead to an approved rule, threshold, owner, steward workflow, and remediation tracking for ownership currency. Simply celebrating the duplicate-rate improvement misses the real trust gap.

• Duplicate-rate focus fails because accuracy for territory assignment depends on current ownership, not just fewer duplicate accounts.
• Refresh frequency fails because loading outdated ownership data more often does not make the data fit for use.
• One-time cleanup fails because it treats a recurring governed quality issue as a temporary repair rather than changing measurement and accountability.

Question 6

Topic: Data Quality Governance

A bank’s data quality platform can measure 120 rules for customer and account data, including completeness, validity, and timeliness. The rules were built by IT from profiling results, but no business area has approved thresholds, ranked the rules by risk, or accepted accountability for remediation priorities. Regulators have recently questioned the reliability of customer risk reports. What is the best governance decision?

Options:

• A. Assign business owners to approve risk-ranked rules and thresholds

• B. Ask IT to tune the measurement jobs for fewer false positives

• C. Require data stewards to correct every failed record immediately

• D. Publish all 120 rule results on an enterprise dashboard

Best answer: A

Explanation: Technically measurable rules are not fully governed until accountable business roles approve what each rule means, how much variation is tolerable, and which failures matter most. In this scenario, the main gap is not measurement capability; it is business ownership, risk ranking, and threshold approval under regulatory pressure. A governance response should assign decision rights to appropriate data owners, involve stewards in defining and monitoring rules, and prioritize remediation based on business and compliance impact. That creates an auditable basis for reporting reliability and prevents IT profiling results from becoming unmanaged de facto policy.

Tuning jobs, publishing scores, or forcing cleanup may help later, but they do not resolve who owns the rules or how risk-based priorities are set.

• Technical tuning may improve rule precision, but it does not establish business-approved thresholds or accountability.
• Dashboard publishing increases visibility, but unowned and unranked metrics can create noise rather than governed action.
• Immediate correction treats symptoms and may waste effort on low-risk failures before priorities are agreed.

Question 7

Topic: Data Quality Governance

A data governance council reviews the monthly quality scorecard for the Customer Legal Name data element.

Which governance focus best addresses the scorecard finding?

Options:

• A. Assign more stewards to correct records

• B. Remediate the web signup source process

• C. Redefine the legal-name standard

• D. Add a new scorecard monitoring control

Best answer: B

Explanation: A quality scorecard should drive governance attention to the cause of the quality gap, not just the visible symptom. Here, the definition is already approved, the target is clear, and other sources meet the standard. The poor score is concentrated in web signup, and the root-cause note identifies a capture process that permits nicknames and lacks evidence. Governance should therefore direct ownership and remediation toward the source process, such as changing entry rules, validation, evidence requirements, and accountability for preventing defects at creation.

Adding stewardship capacity would treat the backlog after defects occur. Adding another control would measure the issue, but the scorecard already provides enough evidence to act.

• Definition change fails because the standard is approved and other sources can meet it.
• More stewardship fails because manual correction is already high and does not prevent new defects.
• More monitoring fails because the existing scorecard identifies a clear process root cause.

Question 8

Topic: Data Quality Governance

A data quality team implements a technically valid rule requiring every customer record to have a verified phone number. The rule is causing 38% of corporate customer records to fail, but relationship managers state that email is the approved contact channel for many corporate accounts. The customer data owner role is still being formalized, and governance policy says quality thresholds must reflect business fitness for purpose and risk. What is the best governance decision?

Options:

• A. Convene the customer stewardship group to define segment-based thresholds and approve ownership

• B. Keep the rule unchanged because it is technically valid

• C. Ask the ETL team to suppress corporate customer failures

• D. Remove phone number verification from all customer quality controls

Best answer: A

Explanation: Data quality governance should connect rules and thresholds to business fitness for purpose, not only technical validity. A verified phone number may be valid for some customer processes, but the stem shows that corporate accounts can legitimately use email as the approved contact channel. The governance response should involve the appropriate business stewardship forum, clarify decision rights for the customer domain, define segment-based expectations, and document approved thresholds or exceptions. This preserves accountability and avoids treating a business rule conflict as only a technical defect.

A technically correct rule can still be poor governance if it blocks useful business activity or measures the wrong requirement for a specific use case.

• Technical validity only fails because a rule can be syntactically or operationally valid while still unfit for the business purpose.
• ETL suppression hides quality results without establishing business approval, ownership, or an auditable threshold decision.
• Control removal overcorrects by eliminating a potentially valid control for customer segments or processes that do require phone verification.

Question 9

Topic: Data Quality Governance

A data engineering team adds a rule that rejects any customer record when the phone number is blank. The rule works correctly and reduces blank values, but the digital sales team reports that many valid online customers provide email only, and rejected records are delaying order follow-up. What is the best governance response?

Options:

• A. Have the data owner and stewards revise the quality threshold based on sales use cases

• B. Disable all phone-number validation until sales confirms every requirement

• C. Keep the rule because it improves the measured completeness score

• D. Ask the database administrator to make the phone field mandatory

Best answer: A

Explanation: Data quality rules should be governed against business fitness for purpose. A technically valid rule can still be inappropriate if it blocks legitimate business activity or measures the wrong outcome. In this case, the rule improves phone-number completeness but harms the sales process because email-only customers are valid for that channel. The appropriate response is to involve the accountable data owner and relevant stewards to confirm business requirements, revise the threshold or condition, and document the approved rule. The revised control might distinguish channel-specific requirements rather than treating all customer records the same. Technical teams can implement the rule, but business accountability determines what level of quality is fit for each use.

• Completeness score focus fails because a better metric is not useful if it reduces business value or rejects valid records.
• Mandatory field change treats a governance decision as a database constraint and ignores channel-specific requirements.
• Disable all validation overcorrects; the better response is to refine and approve the rule, not abandon quality control.

Question 10

Topic: Data Quality Governance

A data governance council can escalate one unresolved data quality issue for executive attention this month. The data quality lead asks which scorecard evidence most strongly supports prioritizing an issue for governance action. Which evidence is the best fit?

Options:

• A. The scorecard added 15 new data quality rules this quarter.

• B. Address completeness is 93% against a 95% target.

• C. Profiling coverage reached 100% for the customer domain.

• D. Critical tax ID defects block invoicing and have overdue remediation.

Best answer: D

Explanation: Data quality scorecards are most useful for governance decisions when they connect measurement to business consequence and accountability. Evidence that a critical data element is causing blocked invoicing, and that remediation is overdue, shows risk, value impact, and a need for decision rights or escalation. A governance forum should prioritize issues where the scorecard indicates material business harm, policy or threshold breach, weak ownership, or stalled remediation. Measures such as rule counts or profiling coverage can show activity and maturity, but they do not by themselves prove that an issue requires governance action.

• Rule count measures scorecard expansion, not whether a specific quality issue is harming business outcomes.
• Profiling coverage shows assessment progress, but it does not show unresolved risk or ownership failure.
• Completeness gap is relevant, but without impact, criticality, trend, or stalled remediation, it is weaker escalation evidence.

Continue in the web app

Use IT Mastery for interactive DAMA CDMP Data Governance Specialist practice with mixed sets, timed mocks, topic drills, explanations, and progress tracking.

Try DAMA CDMP Data Governance Specialist on Web

Related focused pages

• Free DAMA CDMP Data Governance Specialist Full-Length Practice Exam
• Data Governance Foundations and Principles
• Governance Strategy Business Case and Value
• Governance Operating Model and Organization
• Roles Responsibilities and Decision Rights
• Policies Standards Rules and Controls
• Governance Issue Management and Resolution
• Glossary and Metadata Governance
• Security and Compliance Alignment
• Master Reference and Architecture Governance
• Governance Metrics Maturity and Improvement
• Governance Program Operations