DMF-1220 Exam Dumps - Data Management Fundamentals

SPARC published their three-schema approach to database management. The three key components were:

Archiving is the process of moving data off immediately accessible storage media and onto media with lower retrieval performance.

The IBM Data Governance Council model is organized around four key categories. Select the answer that is not a category.

The process of building architectural activities into projects also differ between methodologies. They include:

Data modelling tools are software that automate many of the tasks the data modeller performs.

Value is the difference between the cost of a thing and the benefit derived from that thing.

The language used in file-based solutions is called MapReduce. This language has three main steps:

Within projects, conceptual data modelling and logical data modelling are part of requirements planning and analysis activities, while physical data modelling is a design activity.

The goals of Metadata management include:

Data quality issues only emerge at initial stages of the data lifecycle.

An effective team is based on two simple foundations: trust and a common goal.

Corporate Information Factory (CIF) components include:

Different types of product Master Data solutions 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.

DMMA ratings represent a snapshot of the organization’s capability level.

Data access control can be organized at an individual level or group level, depending on the need.

Control activities to manage metadata stores include:

Data flows map and document relationships between data and locations where global differences occur.

The accuracy dimension has to do with the precision of data values.

Deliverables in the Metadata Management context diagram include:

The goals of data security practices is to protect information assets in alignment with privacy and confidentiality regulations, contractual agreements and business requirements. These requirements come from:

Test environments serve many uses:

To build models, data modellers heavily rely on previous analysis and modelling work.

The ethics of data handling are complex, but is centred on several core concepts. Please select the correct answers.

When constructing an organization’s operating model cultural factors must be taken into consideration.

Please select the incorrect item that does not represent a dimension in the Data Values category in Data Quality for the Information age.

When constructing models and diagrams during formalisation of data architecture there are certain characteristics that minimise distractions and maximize useful information. Characteristics include:

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:

Small reference data value sets in the logical data model can be implemented in a physical model in three common ways:

Data and enterprise architecture deal with complexity from two viewpoints:

Valuation information, as an example of data enrichment, is for asset valuation, inventory and sale.

The Data Warehouse (DW) is a combination of three primary components: An integrated decision support database, related software programs and business intelligence reports.

Common OLAP operations include:

In gathering requirements for DW/BI projects, begin with the data goals and strategies first.

Data management organizational constructs include the following type of model.

Data modelling tools and model repositories are necessary for managing the enterprise data model in all levels.

A System of Reference is an authoritative system where data consumers can obtain reliable data to support transactions and analysis, even if the information did not originate in the system reference.

Metadata is described using a different set of categories, not including:

Business rules describe why business should operate internally, in order to be successful and compliant with the outside world.

Logical abstraction entities become separate objects in the physical database design using one of two methods.

The data in Data warehouses and marts differ. Data is organized by subject rather than function

Information architecture is the process of creating structure for a body of information or content. It includes the following components:

Malware types include:

Poorly managed Metadata leads to, among other, redundant data and data management processes.

Types of metadata include:

The deliverables of the data modelling process include:

Time-based patterns are used when data values must be associated in chronological order and with specific time values.

A dimensional physical data model is usually a star schema, meaning there is one structure for each dimension.

Preparation and pre-processing of historical data needed in a predictive model may be performed in nightly batch processes or in near real-time.

The four main types of NoSQL databases are:

Inputs in the data storage and operations context diagram include:

Hierarchical database model is the newest database model

Use business rules to support Data Integration and Interoperability at various points, to:

Business requirements is an input in the Data Warehouse and Business Intelligence context diagram.

Snowflaking is the term given to normalizing the flat, single-table, dimensional structure in a star schema into the respective component hierarchical or network structures.

Enterprise data architecture usually include the following work streams:

The Belmont principles that may be adapted for Information Management disciplines, include:

Enterprise data architecture description must include both [1] as well as [2]

Dimensions of data quality include:

Taxonomies can have different structures, including:

Domains can be identified in different ways including: data type; data format; list; range; and rule-based.

Project that use personal data should have a disciplined approach to the use of that data. They should account for:

Record management starts with a vague definition of what constitutes a record.

Enterprise data architecture project-related activities include:

Companies do not rely on their information systems to run their operations.

When selecting a DMM framework one should consider of it is repeatable.

A metadata repository is essential to assure the integrity and consistent use of an enterprise data model across business processes.

Data profiling also includes cross-column analysis, which can identify overlapping or duplicate columns and expose embedded value dependencies.