Free DAMA CDMP Quality Practice Questions: Metadata Lineage and Quality Evidence

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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: Metadata Lineage and Quality Evidence

A data quality team is investigating recurring disputes about the “active customer” metric used in a scorecard. Sales includes prospects with open opportunities, while Billing includes only customers with current paid contracts. The team also needs to identify who can approve the quality rule and any limits on using the metric in reports. Which action best supports the quality work?

Options:

• A. Add a technical index to the customer table

• B. Recalculate the scorecard using Billing’s extract only

• C. Run a duplicate detection profile on customer records

• D. Create a business glossary entry linked to catalog metadata

Best answer: D

Explanation: Business glossaries and data catalogs support data quality by making meaning, ownership, context, and usage visible. In this case, the defect is not primarily a profiling problem or a performance issue. The dispute comes from conflicting business definitions of “active customer” and uncertainty about who can approve the rule. A glossary entry can define the term, identify the steward or owner, document approved usage, and link to catalog metadata such as datasets, lineage, and related quality rules. That context helps prevent inconsistent scorecard interpretation and supports governed rule approval.

• Duplicate detection may find repeated customer records, but it does not resolve conflicting business definitions or rule ownership.
• Recalculating from Billing alone chooses one source without governed agreement on the metric definition.
• A technical index may improve query performance, but it does not clarify meaning, stewardship, or acceptable use.

Question 2

Topic: Metadata Lineage and Quality Evidence

A customer data quality scorecard reports preferred_address completeness at 92% against a 95% threshold. Sales disputes the result because CRM captures mailing address, while Billing captures service address, and both feed the customer hub. The steward must advise governance before any remediation budget is approved. Which decision best supports quality interpretation without treating metadata as another quality measurement?

Options:

• A. Add an accuracy percentage for preferred_address

• B. Document the address definition, lineage, and rule scope

• C. Cleanse blank address fields in the customer hub

• D. Lower the completeness threshold to 92%

Best answer: B

Explanation: Metadata needed for quality interpretation explains the meaning, origin, and context of a quality result. Here, the 92% completeness score is the quality measurement. The missing interpretive information is metadata: the business definition of preferred_address, the lineage from CRM and Billing, and the scope of the completeness rule. Without that context, governance cannot tell whether the score reflects a real defect, a definition conflict, or an inappropriate rule for the combined hub. A new metric, threshold change, or cleansing activity may be useful later, but it should follow a clear understanding of what was measured and why.

• Extra metric may measure another dimension, but it does not explain the existing completeness result.
• Threshold change changes evaluation criteria without resolving the definition and lineage ambiguity.
• Immediate cleansing treats the score as a defect before confirming whether the rule matches business meaning.

Question 3

Topic: Metadata Lineage and Quality Evidence

A bank is preparing a data quality scorecard for the customer risk rating used in loan approvals. Profiling shows 7% of applications have a blank risk rating after an integration change. The lending team needs defensible monitoring and clear escalation, but the current metadata catalog only lists the target column name and data type. What is the best metadata action to support quality management?

Options:

• A. Add definition, owner, rule, threshold, lineage, transformation, and usage metadata

• B. Record the profiling percentage and close the issue

• C. Add only the source system and extract schedule

• D. Rename the target column to clarify the data type

Best answer: A

Explanation: Quality management needs metadata that makes data understandable, measurable, and governable. For a customer risk rating used in loan approvals, a catalog entry with only a column name and data type is not enough. The bank needs the business definition, data owner or steward, approved quality rule, acceptable threshold, source, lineage, transformation logic, and usage context. These elements support monitoring, escalation, root-cause analysis, and defensible scorecard reporting. The 7% blank rate is useful evidence, but it must be tied to an approved rule and threshold so stakeholders can decide whether the result is acceptable and who must remediate it. Source and schedule metadata help, but they do not fully support accountability or business fitness for purpose.

• Source-only metadata helps trace where data comes from, but it omits business meaning, rule ownership, thresholds, and transformations.
• Profiling-only evidence describes the current defect, but it does not create sustained monitoring or governance accountability.
• Column renaming may improve readability, but it does not address the missing quality rules, lineage, ownership, or usage context.

Question 4

Topic: Metadata Lineage and Quality Evidence

A data quality team is investigating a sudden increase in missing customer_segment values in the sales mart. The defect affects executive revenue reporting, began after a CRM-to-ERP integration change, and violates a steward-approved completeness rule. The team has limited remediation capacity and must identify both the likely break point and the downstream reports affected. Which lineage evidence is the best fit?

Options:

• A. A monthly completeness scorecard for the sales mart

• B. Field-level lineage from CRM through transformations to reports

• C. A list of tables loaded by the nightly batch

• D. A business glossary entry for customer_segment

Best answer: B

Explanation: For a quality investigation and impact analysis, the strongest evidence is lineage that traces the specific data element across sources, mappings, transformations, controls, and consumers. Here, the problem is not just that customer_segment is incomplete; the team must determine where the defect likely entered after an integration change and which executive reports are affected. Field-level lineage is more useful than dataset-level inventory because it shows how the attribute moves and changes across the CRM, ERP integration, sales mart, and reporting layer. It also supports prioritization when remediation capacity is limited. Scorecards and glossary entries are helpful supporting evidence, but they do not show propagation or break points.

• Glossary-only evidence clarifies meaning and ownership but does not trace the attribute through integration logic or consuming reports.
• Scorecard trend evidence confirms the completeness defect but does not show where the defect entered or what it affects.
• Batch table lists show processing scope but are too coarse to identify field-level transformations or downstream report impact.

Question 5

Topic: Metadata Lineage and Quality Evidence

A data quality team publishes a monthly scorecard showing that the Customer Email Validity rule passed 96% of records. Business users question whether the result can be trusted because several campaigns still failed due to bad email addresses. Which metadata should be captured first to make the quality result trusted and actionable?

Options:

• A. Campaign revenue, email vendor cost, unsubscribe rate, click-through rate

• B. Database size, server name, storage tier, backup frequency

• C. Rule definition, scope, lineage, run time, threshold, exceptions

• D. Dashboard owner, chart colors, report refresh schedule, audience list

Best answer: C

Explanation: Quality results become trusted and actionable when the supporting metadata explains the measurement context and the evidence behind it. For a scorecard result, the team needs the rule definition, business meaning, population in scope, data source and lineage, execution timestamp, threshold, pass/fail counts, and exception details. This allows users to see whether the metric tested the same customer population used by campaigns, whether the rule was current, and which failing records require remediation. Without this metadata, a high pass rate can be misleading because the score may exclude relevant data, use an outdated extract, or apply a rule that does not match business fitness for purpose. Operational or cosmetic report details do not establish confidence in the quality evidence.

• Report presentation helps usability, but it does not prove what was measured or identify exceptions for remediation.
• Marketing outcomes may show business impact, but they do not document the quality rule or scorecard evidence.
• Infrastructure facts may support operations, but they do not explain data lineage, rule scope, or failed records.

Question 6

Topic: Metadata Lineage and Quality Evidence

A data quality team profiles the customer_email attribute in the enterprise customer master. Completeness has fallen below the approved 98% threshold. The attribute is classified as personal data, feeds marketing consent workflows and support notifications, and has no visible steward in the catalog. Which action best uses catalog and glossary support to make the quality result actionable?

Options:

• A. Cleanse the failed records and close the issue after correction.

• B. Document a local definition in the marketing report notes.

• C. Link the result to the cataloged field, rule, owner, classification, lineage, and consumers.

• D. Send the profiling output to consuming teams for manual impact assessment.

Best answer: C

Explanation: A data catalog and business glossary make data quality evidence usable by connecting a measured result to business and governance context. In this case, the completeness failure should be tied to the specific data asset and attribute, the approved quality rule and threshold, the steward or owner, the personal-data classification, lineage, and known consumers. That connection supports prioritization, privacy-aware handling, impact assessment, and accountable remediation. A profile result by itself shows that a defect exists, but cataloged metadata shows who must act, which rule was breached, which uses are affected, and what controls may apply.

• Manual routing may alert consumers, but it does not establish governed ownership, rule context, or reusable lineage evidence.
• One-time cleansing treats symptoms and may reduce current exceptions, but it does not create accountability or prevent recurrence.
• Local report notes narrow the context to one consumer and can create conflicting definitions outside governed glossary control.

Question 7

Topic: Metadata Lineage and Quality Evidence

A data quality team publishes monthly quality scores for customer data, but business users distrust the scores. They say they cannot tell what each score means, which source systems were measured, or how the results relate to approved business definitions. What metadata improvement would most directly improve trust in the scores?

Options:

• A. Publish business definitions, rule logic, source lineage, and score ownership

• B. Add more visual indicators to the quality scorecard

• C. Increase the frequency of automated profiling jobs

• D. Cleanse failed records before publishing the scores

Best answer: A

Explanation: Quality scores need supporting metadata to be credible and fit for purpose. When users do not know what a score means, where the measured data came from, or how the score maps to approved business definitions, the main gap is not measurement volume or presentation. The improvement should connect each score to business glossary definitions, quality rules, thresholds, source lineage, calculation logic, and stewardship accountability. This evidence lets users understand whether the score measures the right thing and whether it applies to their business use.

More profiling or cleansing may improve data quality operations, but it does not explain the meaning, provenance, or governance basis of the published scores.

• More profiling can produce additional measurements, but it does not clarify approved definitions or source lineage.
• Better visuals may make a scorecard easier to read, but it does not provide evidence behind the scores.
• Pre-publication cleansing may reduce visible defects, but it can hide quality issues without explaining score meaning or sources.

Question 8

Topic: Metadata Lineage and Quality Evidence

A data quality analyst finds that account_close_date has been left null for branch-closed accounts since May 1. The account status itself is still updated correctly to Closed.

Lineage summary

Which downstream asset is most affected by the defect?

Options:

• A. Branch closure dashboard

• B. Retention purge job

• C. Profitability mart

• D. Marketing suppression list

Best answer: B

Explanation: Lineage impact analysis traces where a defective data element is used and how that use affects business outcomes. The defect is specific to account_close_date, while account status and branch closure events are still reliable. The downstream asset most exposed is the one that uses the defective date as an operational trigger. A null close date can stop the retention clock from starting, causing records to remain outside the intended retention process. Assets that use separate event logs, status values, or unrelated financial fields are less affected by this specific defect.

The key takeaway is to assess impact by actual field dependency, not by general association with the same business subject.

• Closure counts are not the main impact because the dashboard uses a separate branch event log.
• Status-based suppression still works because account status is updated correctly to Closed.
• Profitability reporting is not materially affected because it does not use the defective close date.

Question 9

Topic: Metadata Lineage and Quality Evidence

A data quality analyst profiles a customer subscription table and finds that cancellation_date is blank for 38% of records. The metadata catalog lists the column name, data type, and source system, but it does not include a business definition or the business condition under which the date is required. What is the most direct effect of this metadata gap?

Options:

• A. Defect ownership must default to the database administrator.

• B. Lineage analysis cannot identify the source system.

• C. Profiling cannot calculate the null percentage.

• D. Completeness rules cannot distinguish missing from not applicable.

Best answer: D

Explanation: Metadata needed for quality management includes more than technical column details. A completeness finding is only meaningful when the business definition and applicability rules are known. For cancellation_date, a blank may be valid for active subscriptions but defective for cancelled subscriptions. Without that semantic metadata, the analyst can still profile nulls, but cannot design a defensible quality rule or threshold that separates acceptable blanks from quality defects. The source system is already known, and ownership should be assigned through stewardship and governance based on business process responsibility, not defaulted to technical administration.

• Source lineage is not the gap because the catalog already identifies the source system.
• Profiling limitation is overstated because technical profiling can still count blanks and calculate percentages.
• Technical ownership is not supported because defect ownership should follow business accountability and stewardship responsibilities.

Question 10

Topic: Metadata Lineage and Quality Evidence

A data quality team is reviewing catalog entries after two certified reports show different “active customer” counts. The glossary contains two approved definitions: Sales defines an active customer as “placed an order in the last 12 months,” while Support defines it as “has an open account with no suspension.” Both reports use valid source fields and documented lineage.

What is the most direct quality effect of this glossary conflict?

Options:

• A. Incomplete lineage for report certification

• B. Inconsistent quality measurement across reports

• C. Invalid reference data values in source systems

• D. Duplicate customer records requiring survivorship rules

Best answer: B

Explanation: A glossary conflict affects data quality by undermining shared meaning. Here, the source fields are valid and lineage is documented, but the business term “active customer” has two approved meanings. That means each report can be technically correct while measuring a different customer population. The quality issue is not primarily validity, completeness, or duplication; it is inconsistent interpretation that makes scorecards, thresholds, and business reporting difficult to compare. Resolving the conflict requires governance agreement on the term definition or clearly governed term variants for different business purposes.

• Reference data values are not the issue because the facts say the source fields are valid.
• Lineage certification is not the issue because lineage is already documented.
• Duplicate records are not indicated; conflicting definitions can change counts even when each customer record is unique.

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