Free DAMA CDMP Fundamentals Practice Questions: Data Quality

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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

A retailer sends customer records from its CRM to billing and a monthly retention dashboard. Email addresses are repeatedly blank or malformed after each weekly load, and the dashboard team currently filters those rows out. Business stewards want consistent measurement and accountable remediation before the next integration release. What is the best professional decision?

Options:

• A. Add a footnote explaining the dashboard exclusion

• B. Keep filtering invalid emails from the dashboard

• C. Run a one-time cleanse of invalid email values

• D. Define and govern a reusable email quality rule

Best answer: D

Explanation: Data quality rule definition establishes the business-approved condition that data must meet, such as required format, completeness, or validity for email addresses. In this situation, the defect recurs after each load and affects both operational billing and analytical reporting, so a one-time repair or reporting workaround does not control the issue. The better action is to define the rule with business stewardship, profile current conformance, measure exceptions, and use the results to drive remediation at the source or integration point. Cleansing may still be needed, but it follows from the rule and measurement rather than replacing them. Reporting adjustments can prevent visible errors, but they do not create accountability for data quality.

• One-time cleanse fixes existing bad values but does not prevent or measure recurring defects in future weekly loads.
• Dashboard filtering hides invalid records downstream and can distort retention reporting without addressing the source issue.
• Footnote disclosure may improve transparency, but it does not establish a rule, measurement process, or remediation accountability.

Question 2

Topic: Data Quality

A customer service dashboard shows account status values that are accurate when compared with the source CRM record. However, the dashboard refreshes only once per week, so agents often act on status changes several days after they occur. Which data quality dimension is most relevant to this complaint?

Options:

• A. Timeliness

• B. Consistency

• C. Accuracy

• D. Completeness

Best answer: A

Explanation: Timeliness measures whether data is available within the time frame required for its intended use. In this case, the account status is not described as wrong in the source or missing from the dashboard. The problem is that agents receive it too late to support current customer interactions. Data can be accurate but still unfit for use if it is stale or delivered after the business decision point. The key distinction is currency and availability for action, not correctness of the value itself.

• Completeness would apply if required account status values were missing or records lacked mandatory fields.
• Accuracy is less relevant because the dashboard values match the CRM when checked.
• Consistency would apply if different systems or reports showed conflicting status values at the same point in time.

Question 3

Topic: Data Quality

A bank profiles customer onboarding data before using it for regulatory screening. The tax_identifier field is required by policy. For records where the value is present, the format matches the approved pattern, but 18% of customer records have no value in the field. Which data quality dimension is most directly affected?

Options:

• A. Completeness

• B. Accuracy

• C. Consistency

• D. Validity

Best answer: A

Explanation: Completeness measures whether required data is present. In this scenario, the tax identifier follows the approved format when it exists, so the main defect is not the structure of the value. The decisive fact is that a required field is blank for a portion of customer records. That makes the data insufficient for the intended regulatory screening use, even though the populated values may be valid. Validity would focus on conformance to rules or formats, while accuracy would focus on whether values correctly represent the real-world customer.

• Validity trap fails because the populated tax identifiers match the approved pattern; the main problem is absence, not rule conformance.
• Accuracy trap fails because no evidence says the present identifiers are wrong compared with authoritative sources.
• Consistency trap fails because the facts do not describe conflicting values across systems or records.

Question 4

Topic: Data Quality

A data quality team finds the same invalid product category values in order-entry screens, warehouse tables, and monthly sales reports. Analysts have corrected the report extracts twice, but the defect reappears after each nightly load. What is the best response?

Options:

• A. Create another manual cleansing step in the warehouse load

• B. Add a warning note to the sales reports

• C. Trace lineage to the originating process and correct the upstream rule

• D. Ask each report owner to maintain a separate category mapping

Best answer: C

Explanation: Root-cause analysis in data quality focuses on why a defect is created or reintroduced, not just where it is observed. Because the same invalid categories appear across order entry, warehouse data, and reports after each nightly load, the likely cause is an upstream business rule, capture process, reference value, or integration mapping. Tracing lineage helps identify where the invalid value first enters or is transformed. Remediation should then correct the source process or controlling rule so downstream reports receive better data by design. Repeated cleansing or report-level fixes may reduce visible errors temporarily, but they do not prevent recurrence.

• Report warning may communicate uncertainty, but it leaves the defect-producing process unchanged.
• Manual cleansing treats symptoms in the warehouse and can create recurring operational effort.
• Separate mappings increase inconsistency because each report owner may interpret categories differently.

Question 5

Topic: Data Quality

A data quality team deployed a customer matching rule in April to reduce duplicate customer records for regulatory reporting. The business goal is to keep duplicate customers below 2% while maintaining tax identifier completeness at 97% or higher. The executive dashboard shows only the duplicate-rate trend.

What is the BEST professional decision?

Options:

• A. Treat the improvement as incomplete and lower the duplicate target

• B. Treat the improvement as sustained and close the issue

• C. Treat the improvement as masking a new completeness issue

• D. Treat the improvement as temporary and wait for more months

Best answer: C

Explanation: Quality trend evidence must be interpreted across the relevant quality rules, not only the improved metric. The duplicate rate improved after April and stayed below the 2% target for three months, which could suggest a sustained improvement for that one dimension. However, the same period shows tax ID completeness dropping from about 98% to 93.5%, below the 97% requirement for regulatory reporting. That pattern indicates the remediation may have introduced or exposed a new issue, such as losing or suppressing identifiers during matching or survivorship. The best decision is to keep monitoring both dimensions, investigate the completeness decline, and avoid declaring success based only on the dashboard headline.

• Closing the issue ignores the completeness requirement and creates reporting risk.
• Waiting for more months is not the strongest response because the completeness breach is already visible and material.
• Lowering the duplicate target addresses the wrong metric; duplicates are already within target after the change.

Question 6

Topic: Data Quality

A data quality team reviews monthly monitoring after a customer matching rule was changed in April. The remediation goal was to reduce duplicate customer records without degrading other critical rules.

Which interpretation best fits the trend evidence?

Options:

• A. The improvement is sustained.

• B. The improvement is temporary.

• C. The improvement is masking a new issue.

• D. The improvement is incomplete.

Best answer: C

Explanation: Continuous data quality monitoring should look beyond the remediated rule to related critical measures. The duplicate rate shows a real and sustained reduction after April, so the original matching defect appears improved. However, the missing consent flag rate rises sharply at the same time and remains high. Because the remediation goal included avoiding degradation of other critical rules, the evidence indicates that one quality gain may be hiding a new problem elsewhere in the customer onboarding process. A sustained improvement in one metric is not enough when another monitored control worsens materially.

• Sustained improvement misses the increased consent flag defect, which violates the no-degradation goal.
• Temporary improvement does not fit because the duplicate rate stays low for three consecutive months.
• Incomplete improvement would fit a partially reduced duplicate issue, but the duplicate metric itself meets the apparent remediation pattern.

Question 7

Topic: Data Quality

A bank profiles Customer Date of Birth in its CRM after branch staff report problems with age-based eligibility checks. The data steward reviews the scorecard.

Which data quality dimension is the primary issue?

Options:

• A. Completeness

• B. Validity

• C. Uniqueness

• D. Accuracy

Best answer: D

Explanation: Accuracy concerns whether data correctly represents the real-world object or an authoritative source. In the scorecard, the decisive finding is that 8.7% of birth dates do not match the verified ID captured at onboarding. The field is fully populated, mostly conforms to format and range rules, has few duplicate-related issues, and meets the refresh SLA. That points to incorrect values rather than missing values, invalid formats, duplicate records, or stale data.

• Completeness does not fit because the field is 100% populated.
• Validity does not fit because almost all values pass format and date-range rules.
• Uniqueness does not fit because the duplicate check shows only a small possible duplicate rate, not the main defect.

Question 8

Topic: Data Quality

A retailer is preparing a customer data feed for a new loyalty analytics dashboard. Recent samples show inconsistent email formats, missing postal codes, and duplicate customer IDs. Business stakeholders need measurable readiness criteria before launch, stewards need a worklist of defects to resolve, and leadership wants ongoing visibility after the feed goes live. What is the best data quality management approach?

Options:

• A. Create a new physical data model for the loyalty dashboard

• B. Load the feed and let dashboard users report defects informally

• C. Encrypt customer identifiers before running the dashboard refresh

• D. Profile the feed, define rules and thresholds, issue exceptions, and publish a scorecard

Best answer: D

Explanation: Data quality management uses several connected practices. Profiling discovers actual data patterns, such as missing values, invalid formats, and duplicate keys. Validation rules express expected conditions, such as required postal codes or valid email patterns. Thresholds define acceptable or unacceptable levels of quality for a specific use. Exception reports give stewards actionable records to investigate and remediate. Scorecards and monitoring show trends, accountability, and readiness over time. In this situation, the organization needs launch criteria, defect worklists, and ongoing visibility, so the approach must cover measurement, control, remediation, and monitoring rather than a single technical fix.

• Informal defect reporting is reactive and does not provide readiness thresholds, controlled validation, or steward worklists.
• Physical modelling may support implementation but does not measure missing values, invalid formats, or duplicates.
• Encryption supports security, but it does not address the stated quality defects or monitoring need.

Question 9

Topic: Data Quality

A retail company has profiled its customer master data and created validation rules for required email, postal code, and consent fields. Business leaders now want a monthly view showing pass rates, agreed target levels, trends, and areas needing stewardship follow-up. Which approach best fits this need?

Options:

• A. Create a monitored data quality scorecard with thresholds

• B. Add validation rules at data entry only

• C. Create an exception report listing failed records

• D. Run one-time profiling to discover field patterns

Best answer: A

Explanation: Data quality measurement uses several related practices. Profiling helps discover current patterns, anomalies, and candidate rules. Validation rules test data against agreed expectations. Thresholds define acceptable levels, such as a required pass rate. Exception reports identify specific records that failed and need remediation. A scorecard summarizes quality results over time, often by dimension, rule, data domain, owner, or target level. In this scenario, leaders need monthly pass rates, targets, trends, and stewardship attention areas, so a monitored scorecard is the strongest fit. Detailed exception reports still matter, but they support operational correction rather than executive or governance-level monitoring.

• One-time profiling helps discover issues and rule candidates, but it does not provide recurring target and trend reporting.
• Exception reporting identifies failed records for correction, but it is too detailed for monthly oversight.
• Data-entry validation can prevent some defects, but it does not summarize quality performance across the data set.

Question 10

Topic: Data Quality

A data quality council can fund one improvement cycle this quarter. The council wants to reduce material business risk before the next regulatory submission, address recurring defects, and use existing stewardship and integration capabilities.

Which action is the BEST professional decision?

Options:

• A. Clean all blank legacy order notes because the defect count is high

• B. Prioritize duplicate customer IDs with root-cause remediation and monitoring

• C. Correct product color misspellings because the cleanup is simplest

• D. Standardize dashboard labels because executives see them most often

Best answer: B

Explanation: Continuous data quality improvement should prioritize work by business impact, risk, recurrence, and feasibility, not by defect count or ease alone. The duplicate customer issue has material operational and privacy risk, recurs weekly in an active onboarding feed, and can be improved with existing capabilities within the quarter. That makes it a strong candidate for root-cause correction, stewardship ownership, matching rule improvement, and monitoring to prevent recurrence. Lower-risk or one-time issues may still be tracked, but they should not displace recurring defects that threaten regulatory reporting, customer operations, or privacy obligations.

• Ease over impact: Product color cleanup is feasible, but its business impact and risk are much lower.
• Volume over value: Blank legacy notes may produce many defects, but they are one-time residue with low business use.
• Presentation over source quality: Dashboard label changes improve readability but do not remediate the underlying recurring data defect.

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