Practice — DQ13-001–017
Answer before opening diagnostics.
DQ13-001 — Foundations & Principles · Foundational · Recall
Which statement best captures Chapter 13's definition of Data Quality? - A. The degree to which relevant Data Quality dimensions meet requirements; in short, data that is fit for purpose. - B. Data that contains no null values under any circumstances. - C. Data that is stored in the enterprise system of record. - D. Data that has been standardized into one technical format.
DQ13-002 — Foundations & Principles · Difficult · Understand
Why can the same data be considered high quality by one group and low quality by another? - A. Because each business unit should define different physical data types. - B. Because Data Quality is context driven: different purposes can require different levels or dimensions of quality. - C. Because quality is determined only by the application that stores the data. - D. Because Data Quality rules should never be standardized across an enterprise.
DQ13-003 — Foundations & Principles · Standard · Distinguish
Why does DAMA describe Data Quality Management as a function rather than only a project or program? - A. Because projects are prohibited from correcting Data Quality issues. - B. Because Data Quality should be owned only by a permanent technical team. - C. Because standards, monitoring, issue handling, communication, and improvement must continue as business-as-usual activities. - D. Because a function never contains projects or improvement initiatives.
DQ13-004 — Foundations & Principles · Foundational · Recall
Which is a Chapter 13 business driver for formal Data Quality Management? - A. Eliminating all business process variation. - B. Replacing Data Governance with Data Quality tooling. - C. Centralizing every enterprise database into one platform. - D. Reducing risks and costs associated with poor quality data.
DQ13-005 — Foundations & Principles · Foundational · Recall
Which is a formal Data Quality Management goal in Chapter 13? - A. Define and implement processes to measure, monitor, and report on Data Quality levels. - B. Guarantee that every data element is 100% accurate. - C. Move all Data Quality responsibility from business processes to IT. - D. Correct every historical defect before defining future controls.
DQ13-006 — Foundations & Principles · Foundational · Understand
What does the Criticality principle require? - A. Measure every data element with equal depth before prioritizing. - B. Focus improvement first on data most important to the enterprise and its customers, considering the risk if it is wrong. - C. Focus first on whichever defects are easiest to correct. - D. Treat only Master Data as critical and ignore transactional data.
DQ13-007 — Foundations & Principles · Foundational · Understand
What does the Standards-driven principle emphasize? - A. Use one vendor's default profiling thresholds as enterprise standards. - B. Replace business requirements with technical data types. - C. Express stakeholder Data Quality requirements as measurable standards and expectations wherever possible. - D. Measure quality only after users complain.
DQ13-008 — Foundations & Principles · Standard · Understand
What is the core idea of Objective measurement and transparency? - A. Hide detailed methodology so stakeholders focus only on one score. - B. Use subjective expert opinion instead of repeatable metrics. - C. Report only defects that have already been corrected. - D. Measure Data Quality consistently and share the measures and methodology with stakeholders who judge fitness for purpose.
DQ13-009 — Foundations & Principles · Standard · Distinguish
Which action best reflects the Prevention principle? - A. Change the data-entry process and controls so a recurring defect is not created in the first place. - B. Run a monthly script that repairs the same defect after it appears. - C. Archive old defect reports after each release. - D. Lower the acceptance threshold so fewer records fail.
DQ13-010 — Foundations & Principles · Standard · Distinguish
Which statement best reflects Root cause remediation? - A. Correct the bad records and close the issue immediately. - B. Understand why the defect occurs and change the responsible process or system, not merely the affected records. - C. Add more dashboards without investigating the source of failure. - D. Reclassify the data as non-critical so the defect is no longer measured.
DQ13-011 — Foundations & Principles · Difficult · Apply
A business process creates customer records, but its manager says Data Quality is entirely IT's responsibility. Which Chapter 13 principle is most directly violated? - A. Objective measurement and transparency only. - B. Criticality only. - C. Embedded in business processes: process owners are responsible for the quality of data produced through their processes. - D. Connected to service levels only.
DQ13-012 — Foundations & Principles · Standard · Apply
A DQ team measures defects but has no agreed response or remediation commitment at critical supplier and system boundaries. Which principle is most directly missing? - A. Criticality. - B. Standards-driven. - C. Objective measurement and transparency. - D. Connected to service levels.
DQ13-013 — Dimensions & Rules · Difficult · Apply
Which factor is strongest for identifying a Critical Data Element? - A. Significant impairment to business success, customer outcomes, regulatory obligations, or financial/reputational risk if the element is wrong. - B. The field has the largest storage size. - C. The field is newest in the data model. - D. The field is populated by the greatest number of users.
DQ13-014 — Dimensions & Rules · Standard · Understand
Why does Chapter 13 say Master and Reference Data are usually critical by definition? - A. They are always stored in one database. - B. They are broadly reused and can affect many processes, reports, and controls when wrong. - C. They are exempt from Data Quality measurement. - D. They can never contain duplicates.
DQ13-015 — Dimensions & Rules · Foundational · Recall
Which question is primarily about Validity? - A. Does the value describe the real-world entity correctly? - B. Did the feed arrive by the required time? - C. Does the value conform to the defined type, range, format, precision, or allowed value domain? - D. Is every required record present in the data set?
DQ13-016 — Dimensions & Rules · Foundational · Recall
Which statement best describes Completeness? - A. Whether related keys point to valid parent records. - B. Whether the value matches reality. - C. Whether data arrives within the required elapsed time. - D. Whether all required data is present at the column, record, or data-set level according to applicable conditions.
DQ13-017 — Dimensions & Rules · Foundational · Recall
What does Consistency primarily test? - A. Whether data values agree with each other where a defined relationship or shared meaning requires agreement. - B. Whether each entity is stored only once. - C. Whether each value is current enough for use. - D. Whether every value can be verified against reality.