Free DAMA CDMP Quality Practice Questions: Quality Rules Standards and Requirements

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: Quality Rules Standards and Requirements

A data governance council is reviewing a proposed certification threshold for the customer master: preferred_color must be populated for at least 95% of active customers. Profiling shows the field is currently 62% complete. Billing, service, compliance notices, and marketing all confirm that they do not use this field in any process or decision. What is the primary weakness in the proposed threshold?

Options:

• A. It is unmeasurable.

• B. It is disconnected from business impact.

• C. It is too strict.

• D. It is too vague.

Best answer: B

Explanation: A quality threshold should express an acceptable level of quality for a defined business purpose. Here, the rule names a field, population, and percentage, so it is not vague or unmeasurable. The main problem is that no business process depends on preferred_color; improving completeness for that field would not improve billing, service, compliance, or marketing outcomes. In DAMA-aligned quality practice, data quality is about fitness for purpose, not maximizing scores on unused attributes. A threshold may look precise but still be weak if it is not tied to business impact.

• Too vague does not fit because the field, population, and required percentage are stated clearly.
• Too strict is not the best diagnosis because strictness depends on business tolerance, and no business need is shown at all.
• Unmeasurable does not fit because completeness for preferred_color can be calculated from the customer master.

Question 2

Topic: Quality Rules Standards and Requirements

A hospital analytics team is defining a quality rule for the discharge_disposition_code field used in readmission reporting. Profiling shows that 7% of records contain locally invented codes, and the BI report joins this field to an approved enterprise reference list maintained by the clinical data steward. The reporting team needs a rule that can be approved, monitored, and used to reject invalid values before monthly reporting. Which quality rule is the best fit?

Options:

• A. Require each code to be unique within the monthly file

• B. Require the field to be populated on every discharge record

• C. Require values to exist in the approved reference list

• D. Require the report to refresh within two hours

Best answer: C

Explanation: A valid-value rule should specify the permitted domain for a data element, preferably using an approved reference data source when one exists. Here, the defect is not missing data, duplicate codes, or slow reporting; it is use of locally invented codes that break a BI join to a governed clinical reference list. The strongest rule is therefore a validity check: each discharge_disposition_code must match an active value in the approved enterprise reference list. That rule is clear enough for steward approval, automatable for monitoring, and actionable for pre-reporting rejection or remediation. Completeness, uniqueness, and timeliness rules are useful in other situations, but they do not address the profiled defect or the downstream reporting impact described here.

• Completeness trap misses the visible defect because populated but locally invented codes can still be invalid.
• Uniqueness trap misunderstands the field because many discharge records may legitimately share the same disposition code.
• Timeliness trap targets report delivery speed, not whether the coded values conform to the approved domain.

Question 3

Topic: Quality Rules Standards and Requirements

A retail bank repeatedly finds invalid customer industry codes in its monthly regulatory dataset. Profiling shows the values originate when sales staff create customer records in the CRM using a free-text field. Sales leaders say account creation must remain fast, but valid industry classification is required before the monthly regulatory extract. Which control placement best reduces recurrence while preserving business value?

Options:

• A. Add a CRM-controlled reference list with a pending-classification workflow

• B. Cleanse invalid codes in the regulatory dataset each month

• C. Reject all new account records unless a valid industry code is entered

• D. Publish a scorecard showing invalid-code counts after submission

Best answer: A

Explanation: Control placement should address the root cause as early as practical without creating unnecessary business friction. The defect starts in CRM because free text allows non-standard industry codes, so a controlled reference list prevents new invalid values. Because sales needs fast account creation and classification is only required before the regulatory extract, a pending-classification workflow preserves operational feasibility while creating accountability for resolution before the data is used for reporting. This combines preventive control at capture with a time-bounded corrective workflow. Downstream cleansing and after-the-fact reporting may reveal or repair defects, but they do not reliably stop recurrence.

• Hard rejection over-controls the process because it blocks account creation before the classification is business-critical.
• Monthly cleansing treats symptoms in the reporting dataset but leaves the CRM process generating the same defect.
• After-submission scorecards provide visibility too late to protect the regulatory use case or prevent recurrence.

Question 4

Topic: Quality Rules Standards and Requirements

A sales executive reports that duplicate customer records cause multiple representatives to contact the same account and inflate quarterly pipeline totals. The current request is stated only as “make customer data better.” Which response best translates the concern into a measurable quality expectation with appropriate ownership?

Options:

• A. Run a profiling scan to list duplicate records in the CRM

• B. Define a customer uniqueness rule, threshold, measurement cadence, and accountable business owner

• C. Create a dashboard showing the monthly count of CRM defects

• D. Ask IT to block all records with incomplete address fields

Best answer: B

Explanation: Requirements gathering for data quality converts business pain into fitness-for-purpose expectations. Here, the concern is duplicate customer records affecting sales coordination and pipeline reporting, so the requirement should focus on uniqueness: how a duplicate is defined, what level is acceptable, how often it is measured, and who owns the rule and remediation decisions. Profiling can provide evidence, and dashboards can monitor results, but neither substitutes for an agreed quality requirement. Assigning the issue only to IT also misses business ownership because the business must define what counts as the same customer and what tolerance is acceptable.

• Profiling alone finds candidate duplicates, but it does not establish the approved rule, threshold, cadence, or owner.
• Address blocking targets completeness or validity, not the stated duplicate-customer problem.
• Defect counting reports activity, but without a business-approved expectation it cannot show whether the data is fit for purpose.

Question 5

Topic: Quality Rules Standards and Requirements

A customer onboarding process repeatedly captures country codes that are not in the approved reference data set. The errors are found only after the nightly integration job rejects the records, causing manual rework. Data stewards have approved the valid code list and want to stop the defect from being created. Which control best fits the situation?

Options:

• A. Monthly scorecard reporting on code defects

• B. Corrective cleansing of rejected records

• C. Detective profiling after the nightly load

• D. Preventive validation against the approved code list

Best answer: D

Explanation: Preventive controls are designed to keep quality defects from entering a process. In this case, the valid country codes are already known and approved, and the business goal is to stop invalid values at the point of capture. Validating entered codes against the governed reference data set addresses the recurring defect closest to its source. Detective controls such as profiling and scorecards are useful for finding or reporting defects, and corrective controls repair data after failure, but neither prevents the invalid codes from being created.

• Nightly profiling finds invalid values after the integration job, so it does not stop record rejection.
• Cleansing rejected records repairs bad data after the defect occurs, leaving the process weakness in place.
• Monthly scorecards summarize quality performance, but reporting alone does not enforce the valid code rule.

Question 6

Topic: Quality Rules Standards and Requirements

A customer onboarding process is creating recurring data quality defects. Profiling shows 8% of new records contain invalid country_code values, causing tax calculations to fail in downstream billing. The approved reference list is owned by a data steward and changes monthly. The remediation team has limited capacity, and leadership wants to stop new defects before they enter the pipeline. Which control type best addresses this situation?

Options:

• A. Corrective cleansing of invalid codes after billing failure

• B. Preventive validation against the governed reference list

• C. Detective scorecard reporting invalid codes weekly

• D. Manual review of tax calculation exceptions

Best answer: B

Explanation: The risk is recurring invalid reference data entering at the source, and the business goal is to prevent new defects rather than expand cleanup. A preventive control, such as source-system validation against the governed country_code list, addresses the defect at the point of creation. Because the list changes monthly and has a steward owner, the control should use the governed reference source rather than a locally maintained copy. Detective controls are useful for monitoring, and corrective controls are needed for existing bad records, but neither is the primary response when leadership wants to stop the defect before it enters the pipeline.

• Weekly reporting detects trends, but it allows invalid values to continue entering the process.
• Post-failure cleansing fixes symptoms after billing is affected and increases load on a constrained remediation team.
• Manual exception review may handle urgent cases, but it does not prevent invalid codes at capture.

Question 7

Topic: Quality Rules Standards and Requirements

A bank is defining data quality requirements for customer email, phone, and consent data. The same data supports fraud-case contact, marketing segmentation, regulatory consent reporting, and a mobile customer profile page. Which approach best supports fitness for purpose across these uses?

Options:

• A. Ask the governance council to select standard dimensions and apply one target to all uses.

• B. Engage use-specific owners and stewards to capture critical fields, definitions, impacts, lineage, and rules.

• C. Collect recent service tickets and prioritize cleansing fields with the highest complaint volume.

• D. Have technical teams profile the fields and convert current value patterns into enterprise thresholds.

Best answer: B

Explanation: Quality requirements should be defined from business use and risk. For the same customer data, fraud contact may emphasize timeliness and completeness, marketing may emphasize consent and segmentation consistency, regulatory reporting may require evidence and lineage, and the mobile profile may emphasize accuracy visible to the customer. The right stakeholders include process owners, analytics consumers, compliance or privacy representatives, customer-channel owners, data owners, and stewards. The information needed includes critical data elements, agreed definitions, acceptable thresholds, lineage, controls, issue impacts, and rule ownership. Profiling is useful evidence, but it does not by itself define what level of quality is fit for each purpose.

• Profiling-only approach fails because observed patterns do not establish business tolerance, regulatory evidence needs, or customer impact.
• Single target approach fails because different uses can need different rules, thresholds, and priorities for the same data.
• Ticket-volume approach fails because complaint history is incomplete and favors visible defects over regulatory or analytical risk.

Question 8

Topic: Quality Rules Standards and Requirements

A retailer’s CRM captures customer birth date and validates that it is a real calendar date. In the customer warehouse, the Age Band used by marketing is derived during ETL. Profiling shows many customers move to the wrong Age Band at month-end because ETL uses the load date instead of the campaign as-of date. Where should the primary quality rule be placed to prevent recurrence?

Options:

• A. Warehouse load, to reject all records with changed Age Band values.

• B. Data entry, to make CRM users choose an Age Band.

• C. Transformation, to calculate Age Band from birth date and campaign as-of date.

• D. Reporting, to suppress rows whose Age Band conflicts with prior reports.

Best answer: C

Explanation: A quality rule belongs where the defect can be prevented or detected closest to its point of creation. Here, birth date capture is already valid, and the defect appears when ETL derives Age Band using the wrong date basis. The rule should therefore be applied in the transformation step, where Age Band is calculated from birth_date and the campaign as-of date. Placing the rule later may detect symptoms, but it would not correct the faulty derivation logic.

• Data entry control fails because users are not creating the defective Age Band; they are entering birth date.
• Warehouse rejection is too blunt because Age Band changes can be valid when based on the correct as-of date.
• Reporting suppression hides bad output but leaves the defective transformation in place.

Question 9

Topic: Quality Rules Standards and Requirements

A retailer’s order data is loaded nightly into a pricing analytics warehouse. Profiling shows that 6% of new orders contain invalid product category codes after a recent source-system change allowed free-text entry. The data steward has approved the official category code list, and the order-entry team can make one change before the next monthly close. Rework capacity is limited, and invalid codes cause pricing reports to be rejected. Which quality control is the best first action?

Options:

• A. Validate category codes at order entry against the approved list

• B. Publish a monthly scorecard showing invalid-code percentages

• C. Email a nightly exception report to the pricing analysts

• D. Run a cleanup job to recode invalid warehouse records

Best answer: A

Explanation: Preventive controls act before or during data creation to stop defects from entering the environment. Here, the defect is being created in the source process because free-text entry bypasses the approved reference list. Since the steward has already approved the valid code set and the source team can change the order-entry process before close, enforcing validation at entry best addresses the timing, purpose, and business impact. Detective controls such as reports and scorecards find defects after they occur. Corrective controls such as cleanup jobs repair existing defects, but they do not stop recurrence. The strongest quality decision is to prevent new invalid codes while existing exceptions can be remediated separately.

• Scorecarding is detective and governance-oriented; it shows the defect rate but does not stop rejected reports.
• Cleanup processing is corrective; it repairs bad warehouse values but leaves the source process creating more defects.
• Exception reporting is detective; it alerts analysts after the nightly load rather than preventing invalid capture.

Question 10

Topic: Quality Rules Standards and Requirements

A data quality analyst is gathering requirements for a supplier validation check. Procurement says, “Preferred suppliers must have a current risk rating.” Profiling shows risk_rating is blank for 18% of preferred suppliers, and metadata shows fields for risk_rating and rating_date. Procurement also notes that some new suppliers can operate while assessment is pending. What business rule is still needed before the quality check can be designed?

Options:

• A. Which profiling tool will scan the supplier table

• B. When a risk rating is required and how current it must be

• C. Who will cleanse the existing blank ratings

• D. Which dashboard color represents a failed record

Best answer: B

Explanation: A quality check must be based on an explicit business rule, not only a general expectation. “Preferred suppliers must have a current risk rating” is incomplete because the discussion includes exceptions for new suppliers and uses the undefined term “current.” The analyst needs to confirm when the rating is mandatory, whether pending assessments are allowed, and what age limit makes a rating current. Only then can a validation rule, threshold, and exception handling approach be specified.

Tool selection, scorecard display, and remediation ownership matter later, but they do not define the pass/fail logic for the check.

• Tool selection does not resolve the rule logic; any tool still needs clear pass/fail criteria.
• Dashboard color affects reporting presentation, not the definition of a valid supplier record.
• Cleansing ownership supports remediation, but cleanup cannot be scoped correctly until the defect rule is known.

Continue in the web app

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

Try DAMA CDMP Data Quality Specialist on Web

Related focused pages

• Free DAMA CDMP Data Quality Specialist Full-Length Practice Exam
• Data Quality Foundations and Business Fitness
• Quality Strategy Business Case and Prioritization
• Profiling Discovery and Assessment
• Root Cause Analysis and Remediation
• Monitoring Scorecards and Measurement
• Quality Governance Roles and Stewardship
• Metadata Lineage and Quality Evidence
• Master Reference and Warehouse Quality
• Quality in Data Lifecycle and Operations
• Quality Maturity and Continuous Improvement
• Specialist Cross-Discipline Judgment