DAMA trained experts have made sure to help the potential applicants of DAMA DQ-1220 certification to pass their DAMA DQ-1220 exam on the first try. Our PDF format carries real Data Quality exam dumps. You can use this format of DAMA DQ-1220 Actual Questions on your smart devices.
The DAMA DQ-1220 exam is designed for professionals who are involved in managing, analyzing, and interpreting data. It is particularly useful for data analysts, data scientists, data engineers, data architects, data stewards, and data quality managers. By passing DQ-1220 Exam, you will demonstrate your ability to ensure that your organization's data is accurate, complete, consistent, and well-documented.
With our DQ-1220 study materials, all your agreeable outcomes are no longer dreams for you. And with the aid of our Data Quality DQ-1220 exam preparation to improve your grade and change your states of life and get amazing changes in career, everything is possible. It all starts from our DAMA DQ-1220 learning questions.
The DAMA DQ-1220 Exam consists of 100 multiple-choice questions and you will be given 2 hours to complete it. The questions are designed to test your knowledge of data quality concepts, methodologies, and tools. You will need to have a good understanding of data profiling techniques, data quality metrics, data validation rules, data standardization methods, and data cleansing techniques.
NEW QUESTION # 33
What is the purpose of Data Governance?
Answer: C
Explanation:
The precise DAMA-DMBOK2 purpose of Data Governance is to ensure that data is managed properly according to policies and best practices. DMBOK2 distinguishes this governance purpose from the broader objective of Data Management, which is concerned with obtaining value from data throughout its lifecycle.
Governance establishes the authority and control framework within which operational data-management functions work. This includes strategy, policy, standards, accountability, stewardship, compliance, issue escalation, quality oversight, and decision rights. DMBOK2 specifically identifies governance responsibilities involving policies for metadata, access, security and quality; setting Data Quality and Data Architecture standards; and providing oversight and corrective action.
This distinction is critical for Data Quality. Data Quality Management performs activities such as profiling, measurement, root-cause analysis, cleansing and monitoring. Data Governance determines who has authority, which requirements are mandatory, what thresholds are acceptable, which issues require escalation, and who owns remediation decisions.
Metadata Management supports governance by recording authoritative definitions, lineage, ownership and quality rules. Master Data Management operationalizes governance for shared business entities and reference domains.
Options B, C and D describe legitimate aspects of wider Data Management, but they do not state the specific purpose assigned to Data Governance by DMBOK2.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Introduction; Goals and Principles; Governance Oversight; Chapter 13 - Data Quality and Governance.
NEW QUESTION # 34
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: C
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 35
Critical to the incremental development of the data warehouse is:
Answer: C
Explanation:
A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA- DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.
A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.
This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures.
Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.
Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.
Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.
Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence - Maintain Data Products; Release Management; Incremental Development; Chapter 13 - Quality Monitoring and Change Control.
NEW QUESTION # 36
Emergency contact phone number would be found in which master data management program?
Answer: A
Explanation:
An emergency contact phone number belongs to the Employee master-data domain. Employee Master Data describes relatively stable information about employees that needs to be consistently identified and reused across organizational processes and systems. Contact details associated with an employee-including emergency-contact information-therefore fall naturally within an Employee MDM program rather than Product or Asset MDM.
The broader DAMA Master Data framework identifies employees as one of the recurring enterprise entities that may require coordinated management alongside customers, products, suppliers, locations, and similar core subjects. Published versions of this specific DAMA certification item likewise identify Employee as the correct domain.
Managing this information centrally or through coordinated master-data processes helps prevent conflicting phone numbers, obsolete emergency contacts, duplicate employee identities, and inconsistent values across Human Resources and other authorized systems.
The data is also sensitive personal information. Governance should therefore define ownership, access rights, retention rules, and permitted usage. Quality controls should address completeness, validity, currency, and accuracy, because outdated emergency-contact information may fail precisely when it is needed.
Metadata Management should record definitions and classifications so that "emergency contact phone number" is consistently distinguished from an employee's own mobile or work telephone number.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Domains; Employee Master Data; Data Governance; Chapter 13 - Accuracy, Completeness and Currency.
NEW QUESTION # 37
Data governance represents:
Answer: B
Explanation:
DAMA-DMBOK2 explicitly states that Data Governance represents an inherent separation of duty between oversight and execution. It illustrates the principle through an analogy with financial governance: an auditor exercises oversight over financial processes without personally executing financial management. Similarly, Data Governance ensures that data is properly managed but does not itself perform every operational Data Management activity.
This separation is fundamental because governance must retain sufficient independence to establish rules, monitor compliance, resolve conflicts, assign accountability, and evaluate whether operational teams are managing data according to approved policies and standards.
Option B describes activities in which governance and Data Quality practitioners may collaborate, but it is not the defining governance principle. Option D is also incorrect because federated governance is only one possible operating model; DMBOK2 also recognizes centralized and replicated approaches. Option E describes an organizational implementation, not the conceptual distinction.
Within Data Quality Management, this means that governance may approve quality policies, Critical Data Elements, acceptable thresholds, stewardship structures, and escalation procedures, while DQ analysts and operational teams perform profiling, cleansing, monitoring, and remediation.
Maintaining this division reduces conflicts of interest and establishes accountability for whether data- management activities achieve agreed business objectives.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Essential Concepts; Oversight versus Execution; Governance Operating Models; Chapter 13 - Data Quality Governance and Operational Responsibilities.
NEW QUESTION # 38
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