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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Available Languages: | English |
| Passing Score: | 70% |
| Real Exam Qty: | 80 |
| Related Certifications: | CDMP - Certified Data Management Professional |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple Choice |
| Exam Price: | USD $400 (Full CDMP exam) |
| 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/ |
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DAMA DQ-1220 exam covers a wide range of topics related to data quality, including data profiling, data cleansing, data validation, data governance, and data quality measurement. It is a rigorous exam that requires candidates to have a solid understanding of data quality concepts and the ability to apply them in practical situations. DQ-1220 exam is designed to challenge candidates and assess their ability to solve real-world data quality problems.
The Data Quality certification exam is administered by the Data Management Association International (DAMA), a global organization that provides guidance and support to data management professionals. DQ-1220 exam is intended for individuals who have experience in data management and are seeking to validate their skills and knowledge in the field of data quality management. Successful completion of the DAMA DQ-1220 Exam is an excellent way for professionals to demonstrate their expertise and enhance their career prospects in the field of data management.
DAMA DQ-1220 exam is a highly respected certification program for professionals in the data management field. Data Quality certification validates an individual's expertise in data quality management and demonstrates their commitment to maintaining high standards of data quality. Data Quality certification provides a competitive advantage to individuals seeking to advance their careers in this field and is highly recommended for professionals who are looking to enhance their skills and knowledge in data quality management.
NEW QUESTION # 96
A company converts "Street", "St.", and "Str" to a single approved address representation before customer matching. This is an example of:
Answer: A
Explanation:
The activity is Standardization. Standardization converts semantically equivalent values into a consistent representation so they can be compared, matched, integrated, and interpreted reliably.
In the scenario, Street, St., and Str may represent the same address concept but differ syntactically.
Converting them to an approved standard reduces superficial variation before entity matching.
Standardization is commonly used for names, addresses, telephone numbers, units of measure, dates, product descriptions, geographic references, abbreviations, and coded values. It frequently precedes matching and de- duplication because entity-resolution algorithms perform more effectively when predictable representational differences have already been normalized.
Reference Data and Metadata Management are important supporting disciplines. Reference Data can define approved abbreviations or formats, while metadata documents transformation rules and intended meaning.
Standardization does not prove that the resulting address is factually accurate. It improves consistency and comparability. An address can be perfectly standardized yet belong to the wrong customer.
Therefore, standardization should be combined with validation, verification, matching, and source- improvement controls where appropriate.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Standardization; Cleansing; Matching; Consistency; Reference Data Management.
NEW QUESTION # 97
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
Answer: B
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 # 98
The main difference between a System of Record and a System of Reference is:
Answer: D
Explanation:
DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption.
A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.
Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.
This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.
Option D is incorrect because "System of Record" and "System of Reference" do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.
Reference Topics: DAMA-DMBOK2 Chapter 10 - System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 - Data Quality and Reference/Master Data Management.
NEW QUESTION # 99
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: C
NEW QUESTION # 100
One way of defining ethics is:
Answer: B
Explanation:
The correct principle is "Doing it right when no one is looking." The statement captures the essential distinction between ethical behaviour and mere compliance. Ethical conduct does not depend on observation, enforcement, or the probability of being caught; it requires responsible action because the action itself is appropriate. The answer wording is presented directly in the PDF on page 4.
DAMA-DMBOK2 treats data ethics as an essential component of professional Data Management because organizations routinely make choices about collecting, combining, analyzing, retaining, sharing, and monetizing data. A technically permissible activity may still create inappropriate effects on individuals or expose data to potential misuse.
Data ethics therefore extends beyond security and regulatory compliance. It requires consideration of the impact on people, potential for misuse, and economic value of data. Ethical handling also requires transparency concerning data provenance, intended use, quality limitations, and the consequences of decisions based on data.
Data Quality has an ethical dimension as well. Knowingly using inaccurate, incomplete, misleading, or poorly understood data in consequential decisions can harm customers and other stakeholders. Governance should therefore establish not only compliance controls but also principles for responsible use.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Ethical Principles; Impact on People; Potential for Misuse; Chapter 13 - Reliable and Fit-for-Purpose Data.
NEW QUESTION # 101
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