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The Data Quality certification exam covers a range of topics, including data profiling, data cleansing, data standardization, data monitoring, and data governance. It also covers concepts such as data quality dimensions, data quality rules, data quality metrics, and data quality assessment techniques. DQ-1220 exam is intended for data management professionals who are responsible for ensuring data quality within their organizations, including data analysts, data scientists, data quality managers, and data governance professionals. By obtaining this certification, professionals demonstrate their commitment to ensuring high-quality data, which is essential for making informed business decisions.
DAMA DQ-1220 Exam consists of 100 multiple-choice questions, and candidates are given 90 minutes to complete it. The questions are designed to test the candidate's understanding of data quality concepts, techniques, and best practices. DQ-1220 exam is administered by DAMA International and can be taken online or in person at a testing center. Candidates who pass the exam are awarded the DAMA Certified Data Quality Professional (CDQP) credential, which is valid for three years.
If you can get the certification for the exam, it not only can prove the ability of you but also can improve your competitive force in the job hunting market. DQ-1220 exam braindumps are high quality and accuracy, and we can help you pass the exam in your first attempt, otherwise we will give you refund. Moreover, DQ-1220 test materials contain most of the knowledge points for the exam, and you can pass the exam as well as improve your ability in the process of learning. DQ-1220 exam materials offer you free update for 365 days after payment, and the update version will be sent to your email automatically.
DAMA DQ-1220 certification exam is an internationally recognized certification for professionals in the field of data quality management. Data Quality certification is offered by DAMA International (Data Management Association), a nonprofit professional association that has been a leading industry voice in data management for over 30 years. DQ-1220 Exam is designed to test the knowledge and skills required for effective management of data quality in organizations.
NEW QUESTION # 85
Mapping requirements and rules for moving data from source to target enables:
Answer: B
Explanation:
Source-to-target mapping enables Transformation. DAMA-DMBOK2 treats mapping as closely synonymous with transformation because a mapping defines how data in one source structure will be converted into the structure, format, representation, or value required by the target.
A mapping specification typically identifies the source attribute, target attribute, extraction conditions, target population rules, intermediate staging transformations, calculations, lookup requirements, and any changes required to make the source data conform to the target representation. DMBOK2 specifically explains that mapping sources to targets involves defining the rules for transforming information from one location and format into another.
Extraction simply retrieves data from the source. Loading places data into the target. Transformation is the activity that applies structural, syntactic, semantic, or value-level modifications between those stages.
The Data Quality connection is substantial. Mappings may standardize dates, convert units, harmonize codes, resolve reference values, remove duplicates, or enforce business rules. If mapping metadata is incomplete or incorrect, the transformation process can introduce rather than correct quality defects.
For this reason, source-to-target mapping should be governed, version-controlled, documented as metadata, traceable through lineage, and validated against agreed business definitions and Data Quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Map Data Sources to Targets; Transformation; ETL
/ELT; Metadata Lineage; Chapter 13 - Data Cleansing and Standardization.
NEW QUESTION # 86
A data lake and a data warehouse are the same concepts in so far as:
Answer: B
Explanation:
Although Data Lakes and Data Warehouses have materially different architectures, both support the broader objective of making organizational data available for reporting, analysis, analytics, and decision support.
Therefore, option B identifies their legitimate conceptual overlap.
A traditional Data Warehouse generally contains integrated, curated, structured data designed for repeatable Business Intelligence, reporting, and analytical workloads. A Data Lake typically retains larger volumes of raw or less-structured information and supports flexible processing, exploration, Data Science, and advanced analytics. Both can therefore participate in analytical data pipelines even though their approaches to schema, transformation, governance, and consumption differ.
They do not inherently duplicate every item of operational data. Nor must a Data Lake produce dimensional or star-schema models; that modeling approach is more closely associated with traditional warehouse implementations. Governance is required for both rather than being the defining difference between them.
From a Data Quality standpoint, warehouses typically apply substantial cleansing and conformity before consumption. Lakes may retain raw values and defer interpretation, which increases the importance of metadata, provenance, cataloging, profiling, and consumer awareness.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; Big Data; Data Lakes; Analytical Data; Metadata; Chapter 13 - Data Preparation and Fitness for Purpose.
NEW QUESTION # 87
The requirement to enter a username, a password and then a code sent to an authentication app is called:
Answer: C
Explanation:
This is two-factor authentication (2FA). Authentication factors are distinguished by the type of evidence used to verify identity. A username identifies the account but does not constitute a separate authentication factor.
The password represents something the user knows, while the code generated or delivered through an authentication application represents something the user has. Because two different factor categories are involved, the mechanism is two-factor authentication.
DAMA-DMBOK2 discusses multiple-factor identification as a security control for systems containing sensitive information. Its examples include a code returned to a user's mobile device, possession of a hardware device, and biometric factors such as fingerprints or facial recognition. DMBOK2 specifically notes that two-factor identification makes unauthorized account access substantially more difficult.
Three-factor authentication would require a third independent category, typically something the user is, such as a biometric characteristic. The mere fact that a username, password, and code are entered does not create three factors because the username is an identifier rather than authentication evidence.
From a Data Quality perspective, strong authentication protects integrity by reducing the risk that unauthorized users alter governed data, quality rules, metadata, or master records.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authentication; Multiple-Factor Identification; Authorization; Data Security; Data Integrity.
NEW QUESTION # 88
During initial profiling of a Country_Code attribute, the analyst wants to identify unexpected values such as
"UKK", "ENG", and blanks. Which profiling output is most useful?
Answer: C
Explanation:
A frequency distribution is the most useful profiling output because it shows each distinct value and how often it occurs. For a controlled attribute such as Country_Code, this quickly reveals unexpected values, misspellings, blanks, obsolete codes, and disproportionately common defaults.
Data profiling is an analytical process used to examine actual data populations and determine their structure, content, patterns, distributions, and potential defects. Frequency analysis is particularly valuable for categorical attributes because it provides evidence about the real values present rather than relying only on declared metadata.
The result can be compared with an authoritative Reference Data domain. Values outside the approved code set become validity exceptions, while unusual frequencies may indicate mapping defects or default-value problems.
This is also where Metadata Management and Reference Data Management interact strongly with Data Quality. Metadata identifies the expected definition and domain; Reference Data provides the authorized codes; profiling determines whether operational values actually conform.
A data model can show that Country_Code exists and perhaps its datatype, but it does not reveal the actual value distribution currently stored in production.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Frequency Analysis; Validity; Reference Data; Metadata Management.
NEW QUESTION # 89
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: A
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 # 90
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