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DAMA DQ-1220 Certification Exam is an essential certification for individuals involved in data management and data quality assurance. It is an industry-recognized certification that validates an individual's knowledge and understanding of data quality concepts, best practices, and methodologies. Data Quality certification is awarded by DAMA International, a highly respected organization in the industry, and is available at Pearson VUE testing centers worldwide.
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DAMA DQ-1220 Exam is recognized and respected by organizations worldwide. Companies look for skilled professionals who can effectively oversee the data management process to ensure the accuracy of data, reduce errors, and improve decision-making. By earning a DAMA DQ-1220 certification, candidates can demonstrate their expertise in data quality management principles and techniques, which can lead to enhancing their career prospects and salary ranges. Data Quality certification course for DAMA DQ-1220 is intensive, demanding, and thoroughly prepares the candidate to succeed in the highly competitive data quality management field.
DAMA DQ-1220 exam is designed for individuals who work with data on a daily basis, including data analysts, data scientists, and data quality professionals. DQ-1220 exam includes questions on topics such as data profiling, data cleansing, data monitoring, and data governance.
NEW QUESTION # 81
A pensioner who usually receives a quarterly bill of around $300 was sent a $100,000,000 electricity bill.
They were a victim of poor data quality checks in which dimension?
Answer: C
Explanation:
The failed control is principally Reasonableness. DAMA treats reasonableness as the extent to which data values or patterns conform to credible expectations. The current DMBOK2 revision recognizes Reasonableness among the standard Data Quality dimensions, alongside Accuracy, Validity, Completeness, Integrity, Uniqueness, Timeliness, Currency and Consistency.
A $100,000,000 residential electricity bill may be syntactically valid: the field may accept the number, the record may satisfy referential constraints, and the value may have arrived on time. Nevertheless, it is grossly inconsistent with the customer's historical pattern of approximately $300 per quarter. A reasonableness control would therefore compare the amount with expected ranges, historical consumption, statistical limits, peer-group distributions, or business thresholds and flag the value before billing.
This illustrates an important distinction from Accuracy. Accuracy asks whether the stored value correctly represents reality. Reasonableness asks whether the value is credible in context. The absurd magnitude itself is detectable without first obtaining an independently verified "true" bill value.
Practical controls include range checks, deviation-from-history rules, z-score/outlier detection, control limits, and exception thresholds. Such rules should be governed as metadata and owned by an appropriate Data Steward.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Dimensions; Reasonableness; Measurement and Monitoring; Statistical Process Control; Data Quality Rules.
NEW QUESTION # 82
A control chart shows a stable defect rate for several weeks, followed by one point far outside the established control limits. This most strongly indicates:
Answer: B
Explanation:
A point materially outside established control limits is a classic signal of special-cause variation. Statistical Process Control distinguishes routine variation inherent in a stable process from unusual variation introduced by an identifiable event or change.
When a Data Quality process normally produces a stable percentage of invalid records and a sudden observation lies beyond the expected statistical range, investigation should focus on what changed. Possible causes include a new source system, software deployment, changed mapping, revised business rule, unusual batch, or manual processing error.
The current DMBOK2 Chapter 13 revision retains Statistical Process Control concepts while reorganizing them into supporting material and explicitly maps Shewhart/Deming improvement stages to Data Quality processes.
The signal does not prove what caused the defect, nor does it establish that performance has improved or deteriorated permanently. It indicates that the observed variation is sufficiently unusual to justify analysis.
Quality teams should preserve the evidence, correlate the timing with system and process changes, use lineage to identify affected sources, and investigate before adjusting the process unnecessarily.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Control Charts; Common Cause; Special Cause; Continuous Improvement.
NEW QUESTION # 83
A Data Quality rule states that every active supplier must have a valid tax identifier. Who should approve whether the rule is mandatory for the Supplier domain?
Answer: A
Explanation:
Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.
A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.
The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.
This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.
DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance and Stewardship; Chapter 13 - Data Quality Rules; Metadata Management; Supplier Master Data.
NEW QUESTION # 84
In data modelling practice, entities are linked by:
Answer: E
Explanation:
In a data model, entities are linked by relationships. An entity represents a distinguishable business concept or thing about which the organization stores information, while a relationship expresses how one entity is associated with another.
For example, a Customer places an Order, an Order contains Order Lines, and a Product appears on an Order Line. Relationships therefore capture business semantics rather than merely physical implementation details.
Cardinality is an important characteristic of a relationship because it specifies how many instances of one entity may or must be associated with instances of another; however, cardinality is not itself the general mechanism by which entities are linked. DAMA-oriented modelling material identifies relationships as the correct linkage concept.
Indexes and triggers are physical database mechanisms. Processes describe business activities and may interact with entities, but they do not define entity-to-entity structure within a data model.
The Data Quality implications are significant. Properly defined relationships establish expectations for referential integrity. For example, an Order referencing a nonexistent Customer indicates an integrity defect.
Profiling can test orphaned foreign keys, invalid relationship cardinalities, and inconsistent associations.
Relationships and their cardinalities should therefore be documented as metadata and enforced through appropriate database, application, or quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Entities, Relationships and Cardinality; Logical Data Modeling; Chapter 13 - Integrity and Consistency; Metadata Management.
NEW QUESTION # 85
A dataset contains every required customer record, but 8% of the telephone numbers belong to different people. Which statement is correct?
Answer: A
Explanation:
The dataset can be complete while remaining inaccurate. Completeness measures whether the required records and values are present; Accuracy measures whether those values correctly represent reality.
If every customer has a telephone number, the field may achieve 100% completeness. However, if 8% of those numbers belong to other people, the values are inaccurate.
This distinction is one of the most important principles in Data Quality measurement. A populated field is not automatically trustworthy. Similarly, a syntactically valid value is not necessarily accurate.
DAMA-aligned guidance explicitly distinguishes these dimensions and notes that a complete dataset may still contain incorrect values.
The appropriate quality scorecard should therefore present completeness and accuracy separately rather than treating one as evidence of the other.
For high-risk customer contact processes, the business may define independent thresholds for both dimensions. Verification services, customer confirmation, authoritative sources, and exception handling may be needed to improve accuracy after basic completeness has already been achieved.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Accuracy; Data Quality Dimensions; Metrics; Fitness for Purpose.
NEW QUESTION # 86
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