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TESTED 16 Jun 2025
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The process of identifying how different records may relate to a single entity is called:
Data architect: A senior analyst responsible for data architecture and data integration.
A control activity in the metadata management environment includes loading statistical analysis.
In a data warehouse, where the classification lists for organisation type are
inconsistent in different source systems, there is an indication that there is a lack of
focus on:
If the target system has more transformation capability than either the source or the intermediary application system, the order of processes may be switched to ELT – Extract Load Tranform.
Please select the correct name for the PDM abbreviation when referring to modelling.
Content refers to the data and information inside a file, document or website.
Which of these is NOT likely in the scope of Data Governance and Stewardship?
If two data stores are able to be inconsistent during normal operations, then the
integration approach is:
The categories of the Data Model Scorecard with the highest weightings include:
Part of alignment includes developing organizational touchpoints for data governance work. Some examples of touchpoints include: Procurement and Contracts; Budget and Funding; Regulatory Compliance; and the SDLC framework.
An organization will create an uncover valuable Metadata during the process of developing Data Integration and Interoperability solutions.
DBAs and database architects combine their knowledge of available tools with the business requirements in order to suggest the best possible application of technology to meet organizational goals.
Data governance requires control mechanisms and procedures for, but not limited to, facilitating subjective discussions where managers’ viewpoints are heard.
The language used in file-based solutions is called MapReduce. This language has three main steps:
E-discovery is the process of finding electronic records that might serve as evidence in a legal action.
Three classic implementation approaches that support Online Analytical Processing include:
Which model has one Data Governance organization coordinate with multiple Business Units to maintain consistent definitions and standards?
All data is of equal importance. Data quality management efforts should be spread between all the data in the organization.
Effective data management involves a set of complex, interrelated processes that enable an organisation to use its data to achieve strategic goals.
A data governance strategy defines the scope and approach to governance efforts. Deliverables include:
The IT security policy provides categories for individual application, database roles, user groups and information sensitivity.
Uniqueness, as a dimension of data quality, states no entity exists more than once within the data set.
When trying to integrate a large number of systems, the integration complexities can
be reduced by:
Achieving near-real-time data replication, using a source accumulation technique,
triggers on:
XML provides a language for representing both structures and unstructured data and information.
When recovering from multiple system failures, what is the biggest difficulty faced
by a DBA?
An implemented warehouse and its customer-facing BI tools is a technology product.
As an often-overlooked aspects of basic data movement architecture, Process controls include:
A project scope requires the collection, exchange, and reporting of data from multiple in-house custom systems. Documents gathered include business concepts, existing database schemas, XSDs, and reporting layouts. How many models of each layer of abstraction can be expected?
Please select the incorrect item that does not represent a dimension in the Data Values category in Data Quality for the Information age.
Data security issues, breaches and unwarranted restrictions on employee access to data cannot directly impact operational success.
A data warehouse deployment with multiple ETL, storage and querying tools often
suffers due to the lack of:
Repositories facilitate the collection, publishing and distribution of data in a centralized and possibly standardized way. Data is most often used to:
The ISO 11179 Metadata registry, an international standard for representing Metadata in an organization, contains several sections related to data standards, including naming attributes and writing definitions.
If data is not integrated with care it presents risk for unethical data handling. These ethical risks intersect with fundamental problems in data management including: Limited knowledge of data’s origin and lineage; Data of poor quality; Unreliable Metadata; and Documentation of error remediation.
Data stewardship is the least common label to describe accountability and responsibility for data and processes to ensure effective control and use of data assets.
Enterprise data architecture influences the scope boundaries of project and system releases. An example of influence is data replication control.
What type of key is used in physical and sometimes logical relational data modelling schemes to represent a relationship?
No recorded negative ethical outcomes does not mean that the organization is processing data ethically. Legislation cannot keep up with the evolution of the data environment so how do we stay compliant?
The purpose for adding redundancy to a data model (denormalisation) is to:
A e-discovery readiness assessment should examine and identify opportunities for the commercial response program.
Please select the answer that does not represent a machine learning algorithm:
Consistent input data reduces the chance of errors in associating records. Preparation processes include:
Which artifact is the highest level of abstraction in the Enterprise Data Model?
Please select the correct definition of Data Management from the options below.
Data professionals involved in Business Intelligence, analytics and Data Science are often responsible for data that describes: who people are; what people do; where people live; and how people are treated. The data can be misused and counteract the principles underlying data ethics.
Organizations are legally required to protect privacy by identifying and protecting sensitive data. Who usually identifies the confidentiality schemes and identify which assets are confidential or restricted?
A roadmap for enterprise data architecture describes the architecture’s 3 to 5-year development path. The roadmap should be guided by a data management maturity assessment.
Data Management maturity has many goals for accomplishment including having a positive effect on culture. This is important to a Data Governance program for the following reason:
Assessment criteria are broken into levels, and most capability maturity models use five (5) levels. This is important since:
According to the DMBoK2, by creating Data Management Services, IT involves the Data Governance Council:
Who should write the main content for a security policy for an organisation?
Poorly managed Metadata leads to, among other, redundant data and data management processes.
An input in the data architecture context diagram includes data governance.
Please select the answers that correctly describes where the costs of poor quality data comes from.
The ethics of data handling are complex, but is centred on several core concepts. Please select the correct answers.
Data profiling also includes cross-column analysis, which can identify overlapping or duplicate columns and expose embedded value dependencies.
Examples of concepts that can be standardized within the data architecture knowledge area include:
Over a decade an organization has rationalized implementation of party concepts from 48 systems to 3. This is a result of good:
The creation of overly complex enterprise integration over time is often a symptom
of:
Normalisation is the process of applying rules in order to organise business complexity into stable data structures.
You have completed analysis of a Data Governance issue in your organisation and have presented your findings to the executive management team. However, your findings are not greeted warmly and you find yourself being blamed for the continued existence of the issue. What is the most likely root cause for this?
Different levels of policy are required to govern behavior to enterprise security. For example:
In the Data Warehousing and Business Intelligence Context Diagram, a primary deliverable is the DW and BI Architecture.
Disciplines within the enterprise architecture practice does not include:
Which statement best describes the relationship between documents and records?
All organizations have the same Master Data Management Drivers and obstacles.
What position is responsible for the quality and use of their organization’s data assets?
Implementing a BI portfolio is about identifying the right tools for the right user communities within or across business units.
Quality Assurance Testing (QA) is used to test functionality against requirements.
The dependencies of enterprise technology architecture are that it acts on specified data according to business requirements.
The Data Governance Council (DGC) manages data governance initiatives, issues, and escalations.
A change management program supporting Data Governance should focus communication on what?
A goal of data architecture is to identify data storage and processing requirements.
A "Data Governance strategy" usually includes the following deliverables:
The independent updating of data into a system of reference is likely to cause:
The Zachman Framweork’s communication interrogative columns provides guidance on defining enterprise architecture. Please select answer(s) that is(are) coupled correctly:
Functionality-focused requirements associated with a comprehensive metadata solution, include:
A staff member has been detected inappropriately accessing client records from
usage logs. The security mechanism being used is an:
Project that use personal data should have a disciplined approach to the use of that data. They should account for:
The percentage of enterprise computers having the most recent security patch
installed is a metric of which knowledge area?
Obfuscating or redacting data is the practice of making information anonymous ot removing sensitive information. Risks are present in the following instances:
A database uses foreign keys from code tables for column values. This is a way of implementing:
Data management professionals who understand formal change management will be more successful in bringing about changes that will help their organizations get more value from their data. To do so, it is important to understand:
Small reference data value sets in the logical data model can be implemented in a physical model in three common ways:
An Operational Data Mart is a data mart focused on tactical decision support.
Data integrity is the state of being partitioned – protected from being whole.
The failure to gain acceptance of a business glossary may be due to ineffective:
ISO 8000 will describe the structure and organization of data quality management, including:
Machine learning explores the construction and study of learning algorithms.
While the focus of data quality improvement efforts is often on the prevention of errors, data quality can also be improved through some forms of data processing.
Data parsing is the process of analysing data using pre-determined rules to define its content or value.
The data-vault is an object-orientated, time-based and uniquely linked set of normalized tables that support one or more functional areas of business.
Select the areas to consider when constructing an organization’s operating model:
Domains can be identified in different ways including: data type; data format; list; range; and rule-based.
Time-based patterns are used when data values must be associated in chronological order and with specific time values.
An image processing system captures, transforms and manages images of paper and electronic documents.
Which of the following provides the strongest tangible reason for driving initiation of a Data Governance process in an enterprise?
Data lineage is useful to the development of the data governance strategy.
Misleading visualisations could be an example where a base level of truthfulness and transparency are not adhered to.
Factors that have shown to play a key role in the success in the success of effective data management organizations does not include:
How can the Data Governance process in an organisation best support the requirements of various Regulatory reporting needs?
A pensioner who usually receives a quarterly bill of around $300 was sent a
$100,000,000 electricity bill. They were a victim of poor data quality checks in
which dimension?
One of the percentages to measure success of a records management system implantation is the percentage of the identified corporate records declared as such and put under records control.
All assessments should include a roadmap for phased implementation of the recommendations. This is important because:
What are some of the business drivers for the ethical handling of data that Data Governance should satisfy?
Several global regulations have significant implications on data management practices. Examples include:
The implementation of a Data Warehouse should follow these guiding principles:
A data model that consists of a single fact table linked to important concepts of the
business is a:
Wat data architecture designs represent should be clearly documented. Examples include:
Defining quality content requires understanding the context of its production and use, including:
The load step of ETL is physically storing or presenting the results of the transformation in the target system.
Risk classifications describe the sensitivity of the data and the likelihood that it might be sought after for malicious purposes.
Communications are essential to the success of a DMM or Data Governance assessment. Communications are important because:
Data modelling tools and model repositories are necessary for managing the enterprise data model in all levels.
There are three recovery types that provide guidelines for how quickly recovery takes place and what it focuses on.
The roles associated with enterprise data architecture are data architect, data modellers and data stewards.
Please select the correct principles of the General Data Protection Regulation (GDPR) of the EU.
Data profiling is a form of data analysis used to inspect data and assess quality.
There are three basic approaches to implementing a Master Data hub environment, including:
To push up the urgency level requires adding of the sources of complacency or increasing of their impact.
Logical abstraction entities become separate objects in the physical database design using one of two methods.
A deliverable in the data architecture context diagram includes an implementation roadmap.
A point to point interface architecture will, in general, have more or less interfa
formats than a service oriented architecture?
Operationality and interoperability depends on the data quality. In order to measure the efficiency of a repository the data quality needs to be:
A Data Management Maturity Assessment (DMMA) can be used to evaluate data management overall, or it can be used to focus on a single Knowledge Area or even a single process.
Match rules for different scenarios require different workflows, including:
A goal of a Reference and Master Data Management program include enabling master and reference data to be shared across enterprise functions and applications.
There is a global trend towards increasing legislative protection of individual's information privacy. Which of these is an emerging topic related to online ethical behaviours?
Modeling Bid data is a non-technical challenge but critical if an organization that want to describe and govern its data.
Big Data and Data Science Governance should address such data questions as:
What are the three characteristics of effective Data Governance communication?
Change Data Capture is a method of reducing bandwidth by filtering to include only data that has been changed within a defined timeframe.
Data Integration and Interoperability is dependent on these other areas of data management:
A Business Glossary forces a business to adopt a single definition of a business term.
The best DW/BI architects will design a mechanism to connect back to transactional level and operational level reports in an atomic DW.
How can the Data Governance process best support Regulatory reporting requirements?
Business requirements is an input in the Data Warehouse and Business Intelligence context diagram.
Examples of business processes when constructing data flow diagrams include:
In Resource Description Framework (RDF) terminology, a triple store is composed of a subject that denotes a resource, the predicate that expresses a relationship between the subject and the object, and the object itself.
Business metadata focuses largely on the content and condition of the data and includes details related to data governance.
The data warehouse and marts differ from that in applications as the data is organized by subject rather than function.
Data Integrity includes ideas associated with completeness, accuracy, and consistency.
Why is it important to create short-term wins when rolling out a Data Governance initiative?
The better an organization understands the lifecycle and lineage of its data, the better able it will be to manage its data. Please select correct implication of the focus of data management on the data lifecycle.
TESTED 16 Jun 2025