With our DQ-1220 test prep, you don't have to worry about the complexity and tediousness of the operation. As long as you enter the learning interface of our soft test engine of DQ-1220 quiz guide and start practicing on our Windows software, you will find that there are many small buttons that are designed to better assist you in your learning. When you want to correct the answer after you finish learning, the correct answer for our DQ-1220 test prep is below each question, and you can correct it based on the answer. In addition, we design small buttons, which can also show or hide the DQ-1220 Exam Torrent, and you can flexibly and freely choose these two modes according to your habit. In short, you will find the convenience and practicality of our DQ-1220 quiz guide in the process of learning. We will also continue to innovate and improve functions to provide you with better services.
Preparing for the DAMA DQ-1220 exam involves studying the DAMA Data Management Body of Knowledge (DMBoK), which covers all aspects of data management. Additionally, there are numerous study materials and practice exams available online to help individuals prepare for the exam. Taking the DAMA DQ-1220 exam is a great way for data professionals to validate their expertise and gain recognition in the industry.
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.
>> DQ-1220 Valid Test Online <<
The second format of DAMA DQ-1220 exam preparation material is the web-based Data Quality (DQ-1220) practice test. It is useful for the ones who prefer to study online. Exams-boost have made this format so that users don't face the hassles of installing software while preparing for the Data Quality (DQ-1220) certification. The customizable feature of this format allows you to adjust the settings of Data Quality (DQ-1220) practice exams.
To be eligible for the DAMA DQ-1220 Certification Exam, candidates must have a minimum of two years of experience in data management or a related field. They must also complete a training program that covers the exam's content, such as DAMA's Data Quality Fundamentals course. DQ-1220 Exam itself is a computer-based test that consists of 100 multiple-choice questions. Candidates have two hours to complete the exam, and a passing score of 70% is required.
NEW QUESTION # 49
The ethics of data handling, center on several core concepts. They are:
Answer: E
Explanation:
DAMA-DMBOK2 organizes Data Handling Ethics around three fundamental considerations: impact on people, potential for misuse, and the economic value of data. The text explains that data frequently represents individuals and is used in decisions that materially affect them; consequently, organizations have an ethical responsibility to ensure that data is reliable and appropriately managed. It also recognizes that data can be intentionally or unintentionally misused and that its economic value creates questions concerning ownership, access, rights, and permitted use.
These concepts directly intersect with Data Quality. Incorrect or misleading data may result in decisions that adversely affect customers, employees, patients, citizens, or other stakeholders. For that reason, accuracy, completeness, transparency, provenance, and appropriate interpretation are not merely technical characteristics-they can have ethical consequences.
Governance operationalizes these ethical principles through policies, decision rights, stewardship responsibilities, access rules, monitoring, and escalation procedures. Metadata Management supports transparency by documenting meaning, provenance, classification, and permitted use. Master Data Management also has an ethical dimension because consolidated person or customer records may create significant consequences when matching or identity-resolution errors occur.
Privacy and security are important controls, but they do not constitute DAMA's complete set of core ethical concepts.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Impact on People; Potential for Misuse; Economic Value of Data; Chapter 13 - Accuracy, Reliability and Governance.
NEW QUESTION # 50
A source application is modified so that a mandatory product identifier can no longer be left blank. Which type of Data Quality action is this?
Answer: C
Explanation:
The application change is a preventive Data Quality control because it prevents a known defect from being created in the first place.
A mandatory-field control ensures that records cannot be accepted without the required Product Identifier.
This differs from a detective control, which would identify missing identifiers after records had already entered the system.
Preventive controls are generally preferable where the organization controls the point of data creation and the business rule is sufficiently clear. They reduce downstream cleansing, exception handling, reconciliation, and operational rework.
However, making the field technically mandatory is appropriate only if the business requirement genuinely applies to every relevant record. Governance and stewardship should confirm the rule before implementation.
If certain product categories legitimately lack the identifier, the validation should incorporate those conditions instead of enforcing an overly broad requirement.
The Product Identifier may also link the transaction to governed Product Master Data, making integrity and consistency important alongside completeness.
DAMA's Data Quality framework emphasizes defining rules, detecting defects, implementing improvement, and integrating quality with Governance and Master Data disciplines rather than relying solely on after-the- fact cleansing.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Completeness; Data Quality Improvement Lifecycle; Chapter 10 - Product Master Data; Governance.
NEW QUESTION # 51
What is the final step in the development of a business-data-driven roadmap?
Answer: B
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 52
Project costs will increase faster by adding to the data model than adding to the process model because:
Answer: B
Explanation:
Each additional data item creates obligations across multiple lifecycle processes, which is why option D is the most complete answer. DAMA-DMBOK2 treats data as an asset that must be managed throughout an interconnected lifecycle rather than merely stored in a database. Activities include creation or acquisition, validation, integration, transformation, storage, maintenance, access, use, protection, retention, archiving, and disposal.
Consequently, introducing another meaningful data element may require changes to data models, metadata repositories, interfaces, mappings, business rules, validation controls, security classifications, quality measurements, lineage documentation, reporting logic, retention rules, and stewardship responsibilities. The cost is therefore cumulative across several management processes.
Option A identifies only migration. Option B reduces the impact to storage, which is usually a minor component. Option C applies only where a warehouse is involved. Option E identifies one legitimate consideration-security-but security is only one part of the complete management obligation.
The Data Quality implication is substantial. Every additional Critical Data Element may require explicit definitions of validity, completeness, accuracy, timeliness, and consistency, together with monitoring and issue-management procedures. This demonstrates why organizations should avoid collecting data indiscriminately and should manage data according to genuine business value and requirements.
Reference Topics: DAMA-DMBOK2 Chapter 1 - Data Lifecycle; Data as an Asset; Chapter 5 - Data Modeling; Chapter 13 - Data Quality Requirements and Monitoring.
NEW QUESTION # 53
A retail system accepts an order for 750,000 identical office chairs from an individual consumer. The value passes all datatype, domain, and mandatory-field checks. Which additional Data Quality dimension should detect the anomaly?
Answer: C
Explanation:
The appropriate dimension is Reasonableness. A value may comply with technical and business-domain constraints while still being implausible in its operating context. An individual customer purchasing 750,000 office chairs is technically possible, but it is sufficiently unusual that it should trigger review.
Reasonableness controls evaluate whether values and combinations of values fall within credible expectations. These controls often depend on business context, historical behaviour, peer comparisons, statistical limits, or relationships between multiple fields.
The current DMBOK2 revision standardizes the term Reasonableness within its nine standard dimensions.
The broader DMBOK2 dimension framework links reasonableness to whether data should be regarded as credible within the operational context.
Useful controls might compare order quantity against customer type, historical maximums, product availability, typical basket size, or statistical deviation from normal purchasing behaviour.
Reasonableness rules are especially valuable for identifying errors that conventional validation cannot detect.
However, unusual data should not automatically be treated as incorrect; it should usually be flagged for investigation.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Reasonableness; Statistical Validation; Exception Detection; Business Rules.
NEW QUESTION # 54
......
DQ-1220 Study Guide Pdf: https://www.exams-boost.com/DQ-1220-valid-materials.html