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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Related Certifications: | CDMP - Certified Data Management Professional |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Exam Format: | Multiple Choice |
| Exam Price: | USD $400 (Full CDMP exam) |
| Exam Duration: | 60 minutes |
| Passing Score: | 70% |
| Available Languages: | English |
| Real Exam Qty: | 80 |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | Online proctored or Onsite testing center |
| Pre Condition: | No formal prerequisites; recommended experience in data management |
| Official Syllabus URL: | https://dama.org/certification/ |
Our DQ-1220 study question is compiled and verified by the first-rate experts in the industry domestically and they are linked closely with the real exam. Our test bank provides all the questions which may appear in the real exam and all the important information about the exam. You can use the practice test software to test whether you have mastered the DQ-1220 Test Practice materials and the function of stimulating the exam to be familiar with the real exam's pace. So our DQ-1220 exam questions are real-exam-based and convenient for the clients to prepare for the DQ-1220 exam.
DAMA DQ-1220 Exam is a globally recognized certification that measures the knowledge and understanding of data quality management principles and best practices. DQ-1220 exam is specifically designed for professionals who want to improve their data quality management skills and knowledge. The DAMA DQ-1220 Exam assesses candidates on concepts such as data governance, data profiling, and data quality assessment techniques. DQ-1220 exam covers topics such as data quality business case development, data management processes, and data auditing and monitoring.
DAMA DQ-1220 exam covers a wide range of topics related to data quality management, including data profiling, data cleansing, data validation, and data standardization. DQ-1220 Exam also covers key concepts such as data governance, data privacy, and data security. DQ-1220 exam is designed to test the candidate's ability to apply these concepts to real-world scenarios, making it an essential certification for professionals in the data management field.
The DAMA DQ-1220 exam is designed to test the knowledge and skills of professionals who are responsible for managing data quality. DQ-1220 exam covers a range of topics, including data quality assessment, data profiling, data cleansing, and data governance. DQ-1220 exam is divided into multiple-choice questions, and candidates have 90 minutes to complete it.
NEW QUESTION # 110
In data modelling practice, entities are linked by:
Answer: B
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 # 111
What are the types of models that are modelled in data models?
Answer: B
Explanation:
DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.
Category information classifies things-for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.
Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.
This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.
Data models also generate important metadata-definitions, relationships, domains and constraints-which becomes the basis for measurable Data Quality rules.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Types of Data that are Modeled; Chapter 10 - Reference and Master Data; Chapter 13 - Data Quality Rules and Dimensions; Metadata Management.
NEW QUESTION # 112
A Data Quality score is calculated from Completeness, Accuracy, and Timeliness. Why might the dimensions be weighted differently?
Answer: D
Explanation:
Dimensions may be weighted differently because their business impact is not necessarily equal for a particular dataset or process.
For an emergency-response system, Timeliness may be more critical than completeness of optional descriptive fields. For regulatory reporting, Accuracy and Integrity may carry substantially greater risk than minor delays. For a marketing contact list, completeness and currency may dominate.
A composite Data Quality score should therefore reflect business priorities rather than assuming that each dimension contributes identical value. Weighting must be transparent and governed; otherwise a high score in low-impact dimensions could hide serious failure in a critical one.
The first step remains defining business requirements and Critical Data Elements. Once those are established, dimensions, thresholds, and weights can be linked to documented business consequences.
A score should also retain underlying component metrics. A single aggregate percentage is insufficient for diagnosis because two datasets with the same overall score may have very different risk profiles.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Metrics; Scorecards; Business Impact; Critical Data Elements; Quality Dimensions; Thresholds.
NEW QUESTION # 113
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
Answer: D
Explanation:
The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.
Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.
The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.
This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.
The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.
Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.
Reference Topics: DAMA-DMBOK2 - Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.
NEW QUESTION # 114
The requirement to enter a username, a password and then a code sent to an authentication app is called:
Answer: E
Explanation:
This is two-factor authentication (2FA). Authentication factors are distinguished by the type of evidence used to verify identity. A username identifies the account but does not constitute a separate authentication factor.
The password represents something the user knows, while the code generated or delivered through an authentication application represents something the user has. Because two different factor categories are involved, the mechanism is two-factor authentication.
DAMA-DMBOK2 discusses multiple-factor identification as a security control for systems containing sensitive information. Its examples include a code returned to a user's mobile device, possession of a hardware device, and biometric factors such as fingerprints or facial recognition. DMBOK2 specifically notes that two-factor identification makes unauthorized account access substantially more difficult.
Three-factor authentication would require a third independent category, typically something the user is, such as a biometric characteristic. The mere fact that a username, password, and code are entered does not create three factors because the username is an identifier rather than authentication evidence.
From a Data Quality perspective, strong authentication protects integrity by reducing the risk that unauthorized users alter governed data, quality rules, metadata, or master records.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authentication; Multiple-Factor Identification; Authorization; Data Security; Data Integrity.
NEW QUESTION # 115
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