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The DQ-1220 Certification Exam is an essential credential for data management professionals who want to demonstrate their expertise in data quality management. By achieving this certification, professionals can gain a competitive edge in the job market and advance their careers in the fast-paced and ever-evolving world of data management.
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DAMA DQ-1220 exam is a globally recognized certification for professionals seeking to validate their knowledge and expertise in data quality management. DQ-1220 exam is designed to test the candidate's proficiency in various aspects of data quality such as data profiling, data cleansing, data validation, and data governance. The DAMA DQ-1220 Certification is ideal for data quality professionals, data analysts, data architects, data managers, and other professionals who work in the field of data management.
NEW QUESTION # 95
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: D
Explanation:
Web-scale search-engine repositories commonly operate according to BASE: Basically Available, Soft State, Eventually Consistent. BASE is associated with distributed and many NoSQL data architectures where availability, horizontal scalability, and tolerance of distributed processing are prioritized over the immediate transactional consistency expected from traditional ACID systems.
A web crawler collects and indexes content continuously across very large distributed environments. Different nodes or indexes may temporarily contain slightly different versions of information while updates propagate.
The design is acceptable because the system is expected to converge toward consistency rather than guarantee that every replica reflects the identical state at every instant. DAMA-aligned material explicitly contrasts BASE with traditional ACID approaches for large-scale distributed data processing.
ACID-Atomicity, Consistency, Isolation, Durability-is generally associated with transaction-oriented relational systems where individual transactions require strict integrity guarantees. Search indexing has different workload characteristics and can tolerate temporary inconsistency.
The Data Quality implication is that "consistency" must be evaluated according to architectural context.
Temporary divergence can be expected behavior in an eventually consistent platform rather than evidence of a defect, provided convergence occurs within acceptable business thresholds.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Distributed Databases; NoSQL; ACID versus BASE; Availability; Consistency.
NEW QUESTION # 96
The goal of data governance is to enable an organisation to manage data as an asset. To achieve this, the DG programs must be:
Answer: D
Explanation:
DAMA-DMBOK2 explicitly states that a Data Governance program must be sustainable. Governance is not a temporary implementation project that ends after policies, committees, or stewardship roles are established. It is an ongoing organizational capability requiring continuing leadership, sponsorship, ownership, decision rights, and operational integration.
The DMBOK2 governance guidance describes sustainable governance as "sticky": it must survive beyond its initial implementation and become embedded in normal business and Data Management practices.
Sustainable governance depends specifically on business leadership, sponsorship, and ownership.
This distinction matters for Data Quality because quality improvement is likewise continuous. New applications, data sources, business processes, regulatory requirements, and transformations continually create new risks. Governance must therefore continue assigning accountability, approving definitions and quality rules, resolving cross-domain disputes, and overseeing remediation.
Options focused on financial registration or assigning a dollar value confuse data-as-an-asset thinking with formal accounting treatment. Data valuation can support investment decisions, but it is not a prerequisite for Data Governance. Likewise, treating governance as a fixed-duration initiative contradicts the operating model described by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Goals and Principles; Sustainable Governance; Leadership; Sponsorship; Ownership; Chapter 13 - Data Quality Governance.
NEW QUESTION # 97
HTTPS:// indicates that the website is:
Answer: B
Explanation:
An address beginning with HTTPS indicates that the website uses an encrypted security layer for communications between the client and server. DAMA-DMBOK2 explicitly identifies HTTPS as a security technology and states that the https:// prefix indicates that a website is equipped with an encrypted security layer.
HTTPS uses TLS to protect information while it is transmitted across the network. Its principal security objectives include confidentiality of the communication channel, integrity of transmitted information, and authentication of the server through digital certificates. This is especially important when transmitting credentials, personal information, payment details, or other sensitive data.
HTTPS does not indicate what database technology the website uses, whether a content management system is installed, whether third-party cookies are present, or whether language-translation functionality exists.
Those characteristics are independent of transport-layer encryption.
From a Data Management standpoint, encryption in transit is one component of a broader Data Security framework. Sensitive data must also be protected at rest, appropriately classified, access-controlled, monitored, and governed throughout its lifecycle.
Data Quality also benefits indirectly because integrity protection helps prevent unauthorized alteration of values during transmission.
Reference Topics: DAMA-DMBOK2 Chapter 7 - HTTPS; Encryption; Data in Transit; Authentication; Confidentiality; Integrity.
NEW QUESTION # 98
A business driver for the creation of a Data Quality Management program is:
Answer: D
Explanation:
A primary business driver for establishing a Data Quality Management program is to reduce the risks and costs associated with poor-quality data. DAMA-DMBOK2 treats low-quality data as a direct source of operational cost and organizational risk. The Chapter 13 business drivers include increasing the value of organizational data, reducing risks and costs caused by poor quality, improving efficiency and productivity, and protecting organizational reputation.
Poor-quality data generates both visible and hidden costs. Examples include rework, manual correction, incorrect invoices, failed customer interactions, regulatory exposure, inappropriate decisions, integration delays, missed opportunities, and reputational damage. DMBOK2 further emphasizes that low-quality data represents cost and risk rather than value, making Data Quality management necessary throughout the data lifecycle.
A formal DQ program addresses these consequences through defined requirements, profiling, measurement, root-cause analysis, corrective and preventive action, monitoring, and governance. The purpose is therefore broader than making the DQ team efficient or supporting a specific technology initiative such as warehouse consolidation.
The economic and risk-reduction argument also provides the strongest basis for executive sponsorship because improvements can be linked directly to measurable business outcomes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Business Drivers; Costs of Poor Data Quality; Risk Reduction; Data Quality Program; Data Quality Lifecycle.
NEW QUESTION # 99
The primary reason for identifying Critical Data Elements is to:
Answer: B
Explanation:
Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.
Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.
The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.
Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.
Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.
Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.
NEW QUESTION # 100
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