Although our company has designed the best and most suitable DQ-1220 learn prep, we also do not stop our step to do research about the DQ-1220 study materials. All experts and professors of our company have been trying their best to persist in innovate and developing the DQ-1220 test training materials all the time in order to provide the best products for all people and keep competitive in the global market. We believe that the DQ-1220 Study Materials will keep the top selling products. We sincerely hope that you can pay more attention to our DQ-1220 study questions.
| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Available Languages: | English |
| Exam Price: | USD $400 (Full CDMP exam) |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 80 |
| Exam Duration: | 60 minutes |
| Related Certifications: | CDMP - Certified Data Management Professional |
| 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/ |
>> Frenquent DQ-1220 Update <<
DumpsQuestion delivers up to date DQ-1220 exam products and modify them time to time. Latest DQ-1220 exam questions are assembled in our practice test modernizes your way of learning and replaces the burdensome preparation techniques with flexible learning. We accord you an actual exam environment simulated through our practice test sessions that proves beneficial for DQ-1220 Exams preparation. Our DQ-1220 practice tests provide you knowledge and confidence simultaneously. Candidates who run across the extensive search, DumpsQuestion products are the remedy for their worries. Once you have chosen for our DQ-1220 practice test products, no more resources are required for exam preparation.
DAMA DQ-1220 (Data Quality) Exam is an internationally-recognized certification that validates the knowledge and skills of professionals in the field of data quality. DQ-1220 exam is designed to measure a candidate's understanding of the principles and best practices related to data quality management. It covers a wide range of topics, including data profiling, data cleansing, data integration, and data governance.
DAMA DQ-1220 (Data Quality) Certification Exam is an industry-recognized certification that assesses an individual's knowledge and skills related to data quality. DQ-1220 Exam is designed to test the candidate's ability to identify, measure, and improve the quality of data in an organization. Data Quality certification is highly valued in the industry as it demonstrates that the individual has the necessary skills and knowledge to ensure that data is accurate, complete, and consistent.
NEW QUESTION # 33
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:
Answer: C
Explanation:
A proliferation of independently implemented data warehouses, lakes, and poorly governed "data swamps" commonly indicates that the organization has emphasized technology acquisition over establishing an integrated, authoritative view of enterprise data. The issue is not the existence of multiple technologies by itself; distributed platforms can be justified by legitimate architectural requirements. The warning sign is uncontrolled duplication without clear authoritative sources, enterprise architecture, governance, lineage, and consistency controls.
DAMA-DMBOK2 treats Data Architecture as the mechanism for aligning data assets with enterprise strategy and reducing unnecessary fragmentation. A mature architecture identifies authoritative sources, data flows, integration patterns, shared definitions, and the required relationship between operational and analytical stores. The DAMA framework also emphasizes that technologies should support business and data- management objectives rather than dictate them.
From a Data Quality perspective, uncontrolled proliferation creates competing versions of values, inconsistent transformations, duplicate master records, contradictory metrics, and unclear ownership. These conditions undermine consistency and make root-cause analysis difficult.
A "single point of truth" does not necessarily require one physical database. It requires controlled authority and consistent semantics across the landscape.
Reference Topics: DAMA-DMBOK2 - Data Architecture; Data as an Asset; Authoritative Sources; Data Integration; Metadata; Data Quality Consistency.
NEW QUESTION # 34
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 # 35
Discovering and documenting metadata about physical data assets provides:
Answer: E
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 # 36
Before defining a Data Quality metric for a business-critical field, the team should first:
Answer: D
Explanation:
A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.
For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.
The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.
DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.
Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.
Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.
NEW QUESTION # 37
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
Answer: C
Explanation:
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
NEW QUESTION # 38
......
Study DQ-1220 Materials: https://www.dumpsquestion.com/DQ-1220-exam-dumps-collection.html