Three formats of our study material are DAMA DQ-1220 PDF Questions, Desktop Practice Test Software, and a Web-Based Practice Exam. We understand that the learning style of every Data Quality (DQ-1220) exam applicant is different. Therefore, we offer three formats of DQ-1220 Practice Test material. Now every Data Quality (DQ-1220) exam candidate can prepare as per his style by selecting the suitable format.
| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Exam Duration: | 60 minutes |
| Exam Price: | USD $400 (Full CDMP exam) |
| Passing Score: | 70% |
| Related Certifications: | CDMP - Certified Data Management Professional |
| 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/ |
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DAMA DQ-1220 Exam is intended for individuals who are interested in pursuing a career in data quality, as well as for those who are already working in the field and wish to demonstrate their proficiency. DQ-1220 exam is also useful for organizations that want to evaluate the skills of their data quality personnel and identify areas for improvement.
DAMA DQ-1220 Certification Exam is an excellent option for individuals who want to enhance their skills in data quality management. It is a rigorous exam that tests the candidate's knowledge and skills in a wide range of topics related to data quality. By passing the exam, professionals can demonstrate their expertise in the field, which can lead to better job opportunities and higher salaries.
NEW QUESTION # 62
The need to manage data movement efficiently is a primary driver for:
Answer: E
Explanation:
The need to manage data movement efficiently is a primary business driver for Data Integration and Interoperability (DII). DAMA-DMBOK2 states this directly. Modern organizations operate hundreds or thousands of databases, applications, files, services, data stores, external interfaces, and analytical platforms.
Data must continually move among these environments, often across organizational boundaries.
Without disciplined integration management, data movement becomes fragmented, expensive, difficult to monitor, and highly dependent on duplicated point-to-point interfaces. DII addresses this problem by establishing controlled mechanisms for extraction, transformation, messaging, replication, orchestration, APIs, data virtualization, and other forms of information exchange.
From a Data Quality perspective, every movement of data presents an opportunity either to preserve quality or to damage it. Source-to-target mappings must maintain semantic meaning, transformations must be controlled, reference values must remain consistent, and lineage must show how values were altered.
Metadata Management therefore records mappings, interface definitions, transformation rules, and lineage.
Master Data Management uses integration mechanisms to distribute governed master and reference data consistently between systems.
Data Warehousing is a major consumer of integration capabilities, but the broader discipline whose explicit driver is efficient movement across systems is Data Integration and Interoperability.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Business Drivers; Data Integration and Interoperability; Data Movement; Chapter 13 - Consistency, Integrity and Transformation Quality.
NEW QUESTION # 63
Discovering and documenting metadata about physical data assets provides:
Answer: B
Explanation:
Discovering and documenting metadata about physical data assets provides visibility into how data moves and is transformed between systems. This is a core function of technical metadata and data lineage.
DAMA-DMBOK2 states that technical metadata describes the technical characteristics of data, the systems that store it, and the processes that move it within and between systems. Examples include physical table and column names, ETL job information, source-to-target mappings, and lineage documentation with upstream and downstream impact information. The certification question therefore points directly to option D; the same interpretation is independently reflected in published CDMP-oriented material.
This capability is critical to Data Quality because a defect observed in a report or downstream application may not originate in the current system. It may have been introduced through extraction logic, transformation rules, reference-data conversion, aggregation, or loading.
Documented physical metadata allows analysts to trace the affected value backward, identify its source, inspect each transformation, and isolate the point at which the defect occurred. It also supports change-impact analysis: modifying a source field or mapping can reveal which downstream assets may be affected.
Metadata therefore provides the evidence required for systematic root-cause analysis rather than symptom- based correction.
Reference Topics: DAMA-DMBOK2 Metadata Management - Technical Metadata; Source-to-Target Mapping; Data Lineage; Data Integration and Interoperability; Chapter 13 - Root-Cause Analysis and Quality Monitoring.
NEW QUESTION # 64
In 2009, ARMA International published GARP for managing records and information. GARP stands for:
Answer: A
Explanation:
GARP stands for Generally Accepted Recordkeeping Principles. ARMA International developed the framework to describe the characteristics of an effective records and information-management program. The principles address accountability, transparency, integrity, protection, compliance, availability, retention, and disposition.
These principles align closely with DAMA-DMBOK2's treatment of Document and Content Management.
Information must be created, organized, protected, maintained, retrieved, retained, and ultimately disposed of according to business, legal, regulatory, and historical requirements. Records management is therefore not simply about long-term storage. It establishes controls throughout the information lifecycle.
For example, the Integrity principle addresses the authenticity and reliability of records. Availability requires information to be retrievable accurately and efficiently. Retention requires organizations to keep records for justified periods, while Disposition governs appropriate final handling after those obligations expire.
These controls interact directly with Data Quality. Records that cannot be found, trusted, interpreted, or demonstrated to be authentic are not fit for their intended purpose even if they physically exist.
Metadata Management supplies classification, retention, provenance, ownership, and lifecycle metadata necessary to implement these principles consistently.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Records Management; GARP; Retention; Disposition; Integrity; Metadata Management.
NEW QUESTION # 65
A DMZ is bordered by 2 firewalls. These are between the DMZ and the:
Answer: E
Explanation:
A Demilitarized Zone (DMZ) is a controlled network segment positioned between an organization's trusted internal environment and an external, untrusted environment such as the Internet. In the classic architecture described in DAMA-DMBOK2, the DMZ is separated from the Internet by an external firewall and from internal organizational systems by another firewall. Therefore, the correct answer is Internet and internal systems.
DMBOK2 describes the DMZ as an area on the organization's perimeter, with firewall protection separating it from the organization and firewall protection between the DMZ and the Internet. It is commonly used for services or transient data exchanges that must interact with external parties without providing direct access to internal systems.
This arrangement implements defense in depth. Public-facing services can operate in the DMZ while internal databases and sensitive systems remain behind an additional security boundary. A compromise of a DMZ host should therefore not automatically expose the internal network.
For Data Quality, security architecture is relevant because unauthorized modification or corruption can compromise integrity and accuracy. Protecting interfaces and repositories supports trustworthy data processing as well as confidentiality and availability.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Network Security; Perimeter; DMZ; Firewalls; Integrity Protection.
NEW QUESTION # 66
Which of the following is the best example of a 'documented data quality rule'?
Answer: D
Explanation:
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
NEW QUESTION # 67
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