Free DAMA CDMP Quality Practice Questions: Data Quality Foundations and Business Fitness

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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 Foundations and Business Fitness

A manufacturer is preparing a product-safety recall using its customer contact data. Profiling shows that customer IDs are unique and all postal codes pass the approved reference-data check. However, only 74% of customer records have a complete deliverable mailing address, and many missing values are apartment or unit numbers. Which assessment most directly reflects data quality as fitness for business purpose?

Options:

• A. The data is high quality because key fields are unique and valid.

• B. The main issue is technical validity of postal codes.

• C. The data is not fit because recall mailings need deliverable addresses.

• D. The data should be considered poor until every attribute is perfect.

Best answer: C

Explanation: Data quality is judged by fitness for a defined business purpose. In this case, the purpose is sending product-safety recall notices. Unique customer IDs and valid postal codes are useful, but they do not prove the mailing address is complete enough for delivery. Missing apartment or unit numbers can prevent notices from reaching affected customers, creating business, safety, and compliance risk. A fit-for-purpose assessment asks whether the data can support the required outcome at an acceptable level, using relevant rules and thresholds for that use. Technical validity is one input, but it is not the whole quality judgment. Abstract perfection is also not required unless the business purpose demands it.

• Technical-only view fails because valid postal codes do not ensure the full address can be used for recall delivery.
• Wrong defect focus treats postal-code validity as the problem, although the visible defect is address completeness for mailing.
• Perfection standard fails because quality is measured against business need and risk, not flawless values in every attribute.

Question 2

Topic: Data Quality Foundations and Business Fitness

A utility company uses customer contact data to send outage restoration notices. Profiling shows that 17% of active customer records have a blank mobile phone number, while populated numbers pass format checks and are loaded before the notification process starts. Customer service owns the capture process and has approved a rule that active accounts should have a mobile number when the customer has consented. Which quality dimension is the best focus for the first scorecard metric?

Options:

• A. Uniqueness

• B. Completeness

• C. Timeliness

• D. Validity

Best answer: B

Explanation: Completeness measures whether required data is present for its intended business use. Here, the business impact is missed outage notices, the profiling evidence shows blank mobile phone numbers, and the approved rule says active accounts should have a mobile number when consent exists. The populated values already pass format checks, so the first metric should track whether the expected value is present, not whether the value has the right pattern. The load occurs before the notification process, so timeliness is not the observed defect. No duplicate records are described, so uniqueness is not the focus.

• Format validity is not the main issue because populated phone numbers already pass the format checks.
• Load timing does not fit because the data arrives before the notification process starts.
• Duplicate detection is unsupported because no repeated customer or phone records are described.

Question 3

Topic: Data Quality Foundations and Business Fitness

A hospital analytics team finds that the discharge_time field is technically valid for 99.8% of patient records. The care coordination team still cannot use the data to schedule follow-up calls because many records are updated two days after discharge, after the outreach window has passed. Which action best reflects a data quality approach based on fitness for business purpose?

Options:

• A. Replace the field with a standardized timestamp format

• B. Define a timeliness rule tied to the outreach window

• C. Require 100% population of all discharge fields

• D. Accept the field because it passes format validation

Best answer: B

Explanation: Data quality is judged by fitness for the intended business use. In this case, the field has high technical validity, but it fails the care coordination purpose because the value arrives too late to support timely outreach. A suitable quality rule should express the business requirement, such as making discharge_time available within the follow-up scheduling window. That turns the quality concern into a measurable, relevant control. Technical format, completeness, and standardization can support quality, but they do not prove the data is usable for the business process.

• Format-only thinking fails because a valid timestamp can still be unusable if it arrives after the business decision point.
• Abstract perfection fails because requiring every discharge field to be complete does not target the specific outreach need.
• Standardization alone fails because a consistent timestamp format does not address late updates.

Question 4

Topic: Data Quality Foundations and Business Fitness

A utility operations team wants to use a service-point feed to dispatch crews during same-day outage events. The data steward approved these fitness criteria: active service points must have a valid location and a status update no older than 15 minutes.

Which conclusion best applies?

Options:

• A. Acceptable after removing duplicate service-point IDs

• B. Not acceptable because active-point timeliness fails the dispatch need

• C. Acceptable because valid locations exceed 99%

• D. Acceptable for monthly reliability trend reporting

Best answer: B

Explanation: Fitness for purpose means data quality is judged against the intended business use, not against a generic quality score. Same-day dispatch requires crews to act on current active service-point status. Although valid locations are strong and duplicates are low, the decisive criterion is timeliness for active points. With only 82% updated within 15 minutes, the feed does not meet the approved operational requirement. It may still be useful for a less time-sensitive analytical purpose, but that does not make it fit for same-day dispatch.

• Location validity alone fails because accurate coordinates do not compensate for stale active-service status in dispatch.
• Duplicate cleanup alone misses the main defect; duplicate IDs are low while timeliness is below the required level.
• Monthly trend reporting may tolerate different thresholds, but the stated use is same-day operational dispatch.

Question 5

Topic: Data Quality Foundations and Business Fitness

A data profiling run finds that 6% of customer records have a blank date_of_birth. The CRM team says the field is optional for account creation, and the defect has existed for years. Which fact would most directly make this data quality issue material from a business-fitness perspective?

Options:

• A. The source system permits the field to be blank

• B. The field has a high null count in profiling

• C. The issue has appeared in prior monthly reports

• D. Age drives eligibility for a regulated product offer

Best answer: D

Explanation: Data quality is material when it affects fitness for purpose: a decision, process, risk, obligation, or stakeholder outcome that depends on the data. A 6% null rate is evidence of a defect, but it does not by itself show business significance. If date_of_birth determines eligibility for a regulated product offer, missing values can cause incorrect offers, unfair exclusion, compliance exposure, or remediation cost. That connection turns a profiling finding into a business-relevant quality issue.

The key distinction is between measuring a defect and showing why the defect matters to a business use.

• Profile result only shows scale, but a null count alone does not establish business impact.
• Permitted by source may explain the root cause, but it does not show materiality.
• Recurring reports show persistence, but repetition is not the same as business fitness impact.

Question 6

Topic: Data Quality Foundations and Business Fitness

A logistics company is improving the customer address data used for premium delivery scheduling. Profiling shows 92% of addresses pass postal-format validation, but 18% of premium orders still miss the promised delivery window because apartment or loading-dock instructions are absent. The business sponsor wants improvement before peak season, and the data steward can change only the premium-order capture process this quarter. What is the best quality decision?

Options:

• A. Define a fit-for-purpose rule for delivery instructions on premium orders

• B. Wait until enterprise address standards are fully approved

• C. Cleanse the entire historical customer address file

• D. Require all customer addresses to pass postal-format validation

Best answer: A

Explanation: Data quality is fitness for business purpose. Postal-format validity matters, but it does not prove that the address data supports premium delivery scheduling. The visible business harm is caused by missing apartment or loading-dock instructions, and the steward has authority to improve the premium-order capture process now. A practical quality response should define a rule and control for the data needed by that specific process and downstream use, rather than chasing abstract perfection across all customer addresses. The closest trap is format validation: technically valid addresses can still be incomplete for a delivery promise.

• Format-only control misses the fact that many valid postal addresses still lack delivery-critical instructions.
• Whole-file cleansing is broader than the current constraint and does not prevent new premium orders from missing required details.
• Delayed standards work may help later, but it does not address the urgent peak-season business impact within current stewardship capacity.

Question 7

Topic: Data Quality Foundations and Business Fitness

A data steward reviews a recurring customer onboarding issue. Sales reports lost orders when new customer records are created with invalid tax classification codes. Profiling shows 7% invalid codes in the last month, all originating from a free-text field in the onboarding form. Governance has approved a standard reference list, and the process owner can change the form before the next sales cycle. Which classification best fits the quality initiative that should be prioritized?

Options:

• A. Reactive handling of sales order failures

• B. Preventive control at the source process

• C. Detective monitoring through a monthly scorecard

• D. Corrective cleansing of existing customer records

Best answer: B

Explanation: A preventive quality initiative stops defects from being created, usually by improving the source process, control, rule, or data entry design. The facts point to a known root cause: a free-text onboarding field allows invalid tax classification codes. Because governance has approved the reference list and the process owner can change the form before the next cycle, the best priority is to prevent future invalid values at capture. Detective monitoring would find defects after entry, corrective cleansing would repair existing records, and reactive handling would respond only after sales orders fail.

• Monthly scorecard detects and reports the 7% invalid-code rate but does not stop new invalid values from being created.
• Record cleansing corrects current customer data but leaves the free-text root cause in place.
• Order-failure handling reacts to business disruption after the defect has already affected sales.

Question 8

Topic: Data Quality Foundations and Business Fitness

A data quality analyst finds that 18% of customer records in the marketing mart have a missing consent_timestamp. Marketing uses this attribute to decide which customers can receive renewal offers. Which action best connects the defect to business fitness for purpose?

Options:

• A. Link missing timestamps to renewal eligibility and quantify affected customers.

• B. Standardize the timestamp format in the mart.

• C. Classify the issue as a completeness defect.

• D. Add the missing-field percentage to a scorecard.

Best answer: A

Explanation: Data quality is judged by fitness for purpose, not only by the presence of a technical defect. The decisive action is to connect the missing consent_timestamp to the renewal-offer eligibility decision and quantify how many customers may be incorrectly included, excluded, or delayed. That impact evidence helps prioritize remediation, involve the right steward and business owner, and determine whether operational controls are at risk. Naming the dimension or publishing a metric can support quality management, but those actions do not by themselves show how the defect affects a business decision or customer outcome.

• Dimension label only identifies completeness but does not show which renewal decisions or customers are affected.
• Scorecard reporting measures the defect rate but may remain disconnected from decision impact.
• Format standardization addresses a validity or consistency concern, not the missing consent values described.

Question 9

Topic: Data Quality Foundations and Business Fitness

A marketing team cannot determine campaign eligibility for many new customers. Profiling shows 18% of customer records created in the last two weeks have a null consent_status. Lineage review shows the new web signup form stopped requiring the field after a release. The data steward must log the issue so governance can track the quality dimension, measurement, root cause, and remediation. Which entry is the best fit?

Options:

• A. Dimension: validity; measure: allowed-code failure rate; root cause: reference list gap; remediation: add consent codes

• B. Dimension: accuracy; measure: duplicate count; root cause: weak matching; remediation: merge customer records

• C. Dimension: completeness; measure: null rate; root cause: form change; remediation: restore control and backfill values

• D. Dimension: timeliness; measure: load latency; root cause: delayed batch; remediation: increase refresh frequency

Best answer: C

Explanation: The central defect is that a required attribute is absent, so the quality dimension is completeness. The measurement method should quantify the defect directly, such as the percentage of records where consent_status is null. The root cause is not the null value itself; it is the web signup process change that stopped requiring the field. A sustainable remediation addresses both prevention and correction: restore the source control so new records are captured correctly, then backfill or otherwise resolve the affected records according to approved business rules. Accuracy, timeliness, and validity are plausible dimensions in other cases, but they do not match the visible evidence of missing required data.

• Accuracy trap fails because no evidence shows incorrect values or duplicate customer records.
• Timeliness trap fails because the records are present; the defect is a missing attribute, not late delivery.
• Validity trap fails because the issue is null consent_status, not values failing an approved code set.

Question 10

Topic: Data Quality Foundations and Business Fitness

A data steward reviews a customer retention report. For the same customer ID and reporting period, the CRM system shows Status = Active, while the billing system shows Status = Cancelled. Both values are populated, arrived before the reporting cutoff, and there is only one record per system for the customer. What is the main data quality issue?

Options:

• A. Conflicting data

• B. Incorrect data

• C. Duplicated data

• D. Late data

Best answer: A

Explanation: Conflicting data occurs when two or more sources, records, or attributes present different values for the same business fact in the same context. Here, the customer status is populated in both systems, was available on time, and is not duplicated within either system. The key evidence is disagreement between CRM and billing about one customer’s status for the same reporting period. That makes the issue a consistency conflict that needs rule ownership, authoritative-source guidance, or reconciliation logic before the report can be trusted.

• Incorrect data is not established because the facts do not show which value is wrong.
• Late data does not fit because both values arrived before the reporting cutoff.
• Duplicated data does not fit because there is only one record per system for the customer.

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