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Preparation and pre-processing of historical data needed in a predictive model may be performed in nightly batch processes or in near real-time.
Data Governance Office (DGO) focuses on enterprise-level data definitions and data management standards across all DAMA-DMBOK knowledge areas. Consists of coordinating data management roles.
Resource Description Framework (RDF), a common framework used to describe information about any Web resource, is a standard model for data interchange in the Web.
Enterprise data architecture influences the scope boundaries of project and system releases. An example of influence is data replication control.
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
Release management is critical to batch development processes that grows new capabilities.
A deliverable in the data architecture context diagram includes an implementation roadmap.
An enterprise's organisation chart has multiple levels, each with a single reporting
line. This is an example of a:
Issue management is the process for identifying, quantifying, prioritizing, and resolving Data Governance issues. Which of the following are areas where that issues might arise:
The difference between warehouses and operational systems do not include the following element:
A catastrophic system failure due to processing attachments that are too large may
be solved by:
Data modelling tools and model repositories are necessary for managing the enterprise data model in all levels.
The data in Data warehouses and marts differ. Data is organized by subject rather than function
How can the Data Governance process best support Regulatory reporting requirements?
The scope and focus of any data governance program depend on organizational needs, but most programs include:
When doing reference data management, there many organizations that have standardized data sets that are incredibly valuable and should be subscribed to. Which of these organizations would be least useful?
What area do you not consider when developing a 'Data Governance operating model?
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.
A limitation of the centralized approach include: Maintenance of a decentralized repository is costly.
Data access control can be organized at an individual level or group level, depending on the need.
The information governance maturity model describes the characteristics of the information governance and recordkeeping environment at five levels of maturity for each of the eight GARP principles. Please select the correct level descriptions:
There are numerous methods of implementing databases on the cloud. The most common are:
When starting a Data Governance initiative it is important to understand what the Business cannot achieve due to data issues because:
A goal of reference and master data management is for data to ensure shared data is:
What ISO standard defines characteristics that can be tested by any organisation in the data supply chain to objectively determine conformance of the data to this ISO standard.
Data quality management is a key capability of a data management practice and organization.
A completely distributed architecture maintains a single access point. The metadata retrieval engine responds to user requests by retrieving data from source systems in real time.
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.
Organizations should evaluate several maturity assessment models for data management, and for Data Governance, before adopting one or before developing a custom maturity assessment model because:
For each subject area logical model: Decrease detail by adding attributes and less-significant entities and relationships.
The database administrator (DBA) is the most established and the most widely adopted data professional role.
Customer value comes when the economic benefit of using data outweighs the costs of acquiring and storing it, as well we managing risk related to usage. Which of these is not a way to measure value?
Business requirements is an input in the Data Warehouse and Business Intelligence context diagram.
The first two steps of the Reference data Change request process, as prescribed DMBOk2, include:
The biggest business driver for developing organizational capabilities around Big Data and Data Science is the desire to find and act on business opportunities that may be discovered through data sets generated through a diversified range of processes.
Where is the best place to find the following metadata: database table names,
column names and indexes?
The process of building architectural activities into projects also differ between methodologies. They include:
The Data Warehouse encompasses all components in the data staging and data presentation areas, including:
Over a decade an organisation has rationalised implementation of party concepts
from 48 systems to 3. This is a result of good:
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.
Deliverables in the data management maturity assessment context diagram include:
Your organization has many employees with official roles as data stewards and data custodians, but they don't seem to know exactly what they're supposed to be doing. Which of the following is most likely to be a root cause of this problem?
The failure to gain acceptance of a business glossary may be due to ineffective:
Differentiating between data and information. Please select the correct answers based on the sentence below: Here is a marketing report for the last month [1]. It is based on data from our data warehouse[2]. Next month these results [3] will be used to generate our month-over-month performance measure [4].
Service accounts are convenient because they can tailor enhanced access for the processes that use them.
The language used in file-based solutions is called MapReduce. This language has three main steps:
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.
Data science involves the iterative inclusion of data sources into models that develop insights. Dat science depends on:
Please select the user that best describes the following description: Uses the business glossary to make architecture, systems design, and development decisions, and to conduct the impact analysis.
The creation of overly complex enterprise integration over time is often a symptom
of:
Subtype absorption: The subtype entity attributes are included as nullable columns into a table representing the supertype entity
The load step of the ETL is physically storing or presenting the results of the transformation into the source system.
A data model that consists of a single fact table linked to important concepts of the
business is a:
Information gaps represent enterprise liabilities with potentially profound impacts on operational effectiveness and profitability.
A synonym for transformation in ETL is mapping. Mapping is the process of developing the lookup matrix from source to target structures, but not the result of the process.
Volume refers to the amount of data. Big Data often has thousands of entities or elements in billions of records.
Confidentiality classification schemas might include two or more of the five confidentiality classification levels. Three correct classifications levels are:
Which of the following is NOT a preventative action for creating high quality data?
Elements that point to differences between warehouses and operational systems include:
Emergency contact phone number would be found in which master data
management program?
Changes to reference data do not need to be management, only metadata should be managed.
Select the areas to consider when constructing an organization’s operating model:
Content needs to be modular, structured, reusable and device and platform independent.
To build models, data modellers heavily rely on previous analysis and modelling work.
Data Storage and Operations: The design, implementation and support of stored data to maximize its value.
The ethics of data handling are complex, but is centred on several core concepts. Please select the correct answers.
The advantage of a decentralised Data Governance model over a centralised model is:
The roles associated with enterprise data architecture are data architect, data modellers and data stewards.
As an often-overlooked aspects of basic data movement architecture, Process controls 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.
Data governance can be understood in terms of political governance. It includes the following three function types:
Gathering and interpreting results from a DMM or Data Governance assessment are important because:
Effectiveness metrics for a data governance programme includes: achievement of goals and objectives; extend stewards are using the relevant tools; effectiveness of communication; and effectiveness of education.
Bias refers to an inclination of outlook. Please select the types of data bias:
The business glossary application is structured to meet the functional requirements of the three core audiences:
ANSI standard 859 has three levels of control of data, based on the criticality of the data and the perceived harm that would occur if data were corrupt or otherwise unavailable, including:
Logical abstraction entities become separate objects in the physical database design using one of two methods.
Effective data management involves a set of complex, interrelated processes that disable an organization to use its data to achieve strategic goals.
Data asset valuation is the process of understanding and calculating the economic value of data to an organisation. Value comes when the economic benefit of using data outweighs the costs of acquiring and storing it, as
The purpose of enterprise application architecture is to describe the structure and functionality of applications in an enterprise.
One of the deliverables in the Data Integration and Interoperability context diagram is:
Improving data quality requires a strategy that accounts for the work that needs to be done and the way people will execute it.
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.
Which statement best describes the relationship between documents and records?
Because Data Governance activities require coordination across functional areas, the DG program must establish an ___________ that defines accountabilities and intersections.
An advantage of a centralized repository include: High availability since it is independent of the source systems.
Data Integration and Interoperability (DII) describes processes related to the movement and consolidation of data within and between data stores, applications and organizations.
Obfuscating or redacting data is the practice of making information anonymous ot removing sensitive information. Risks are present in the following instances:
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:
As part of its transformation, the organization must identify and respond to different kinds of roadblocks. Please select the answer that is not a roadblock:
Communications are essential to the success of a DMM or Data Governance assessment. Communications are important because:
Please select correct term for the following sentence: An organization shall assign a senior executive to appropriate individuals, adopt policies and processes to guide staff and ensure program audibility.
Time-based patterns are used when data values must be associated in chronological order and with specific time values.
A goal of reference and master data is to provide authoritative source of reconciled and quality-assessed master and reference data.
A goal of metadata management is to manage data related business terminology in
order toc
In an information management context, the short-term wins and goals often arise from the resolution of an identified problem.
Traditional tool sin data visualtization have both a data and a graphical component. Advanced visualization and discovery tools use in-memory architecture to allow users to interact with the data.
According to the DMBoK, Data Governance is central to Data Management. In practical terms, what other functions of Data Management are required to ensure that your Data Governance programme is successful?
The DW encompasses all components in the data staging and data presentation areas, including:
What are some of the business drivers for the ethical handling of data that Data Governance should satisfy?
Enterprise service buses (ESB) are the data integration solution for near real-time sharing of data between many systems, where the hub is a virtual concept of the standard format or the canonical model for sharing data in the organization.
Quality Assurance Testing (QA) is used to test functionality against requirements.
Content refers to the data and information inside a file, document or website.
Content management includes the systems for organizing information resources so that they can specially be stored.
Use business rules to support Data Integration and Interoperability at various points, to:
Once the most critical business needs and the data that supports them have been identified, the most important part of the data quality assessment is actually looking data, querying it to understand data content and relationships, and comparing actual data to rules and expectations.
Confirming and documenting understanding of different perspectives facilitate:
The IT security policy provides categories for individual application, database roles, user groups and information sensitivity.
An organization can enhance its Data Governance program and thereby improve its approach to enterprise data management. This is important for the following reason:
ISO 8000 will describe the structure and organization of data quality management, including:
The most informal enterprise data model is the most detailed data architecture design document.
Media monitoring and text analysis are automated methods for retrieving insights from large unstructured or semi-structured data, such as transaction data, social media, blogs, and web news sites.
Every DMM and Data Governance assessment must define how the assessment team will interact with its subjects (after defining the subject/stakeholder list). This is important because:
If two data stores are able to be inconsistent during normal operations, then the
integration approach is:
Data science merges data mining, statistical analysis, and machine learning with the integration and data modelling capabilities, to build predictive models that explore data content patterns.
A point to point interface architecture will, in general, have more or less interfa
formats than a service oriented architecture?
Change only requires change agents in special circumstances, especially when there is little to no adoption.
Examples of concepts that can be standardized within the data architecture knowledge area include:
Structural Metadata describe srealtionships within and among resource and enables identification and retrieval.
A content strategy should end with an inventory of current state and a gap assessment.
All DMM and Data Governance assessments should identify its objectives and goals for improvement. This is important because:
Tools required to manage and communicate changes in data governance programs include
A critical step in data management organization design is identifying the best-fit operating model for the organization.
Archiving is the process of moving data off immediately accessible storage media and onto media with lower retrieval performance.
TESTED 02 May 2025