DMF-1220 Exam Questions

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

The accuracy dimension of data quality refers to the degree that data correctly respresents `real- life' entities.

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.

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

Data Integrity includes ideas associated with completeness, accuracy, and consistency.

Validity, as a dimension of data quality, refers to whether data values are consistent with a defined domain of values.

Dimensions of data quality include:

ISO 8000 will describe the structure and the organization of data quality management, including:

Some common data quality business rule types are:

The Shewhart chart contains the following elements:

Issues caused by data entry processes include:

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

Input in the Big Data and data science context diagram include:

Data science involves the iterative inclusion of data sources into models that develop insights. Dat science depends on:

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

MPP is an abbreviation for Major Parallel Processing.

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 interac...

Analytics models are associated with different depths of analysis, including:

Modeling Bid data is a non-technical challenge but critical if an organization that want to describe and govern its data.

Business people must be fully engaged in order to realize benefits from the advanced analytics.

CMA is an abbreviation for Capability Maturity Assessment.

Organizations conduct capability maturity assessments for a number of reasons, including:

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.

Deliverables in the data management maturity assessment context diagram include:

The primary goal of data management capability assessment is to evaluate the current state of critical data management activities in order to plan for improvement.

Decentralized informality can be made more formal through a documented series of connections and accountabilities via a RACI matrix.

Data management organizational constructs include the following type of model.

Factors that have shown to play a key role in the success in the success of effective data management organizations does not include:

Communication should start later in the process as too many inputs will distort the vision.

Disciplines within the enterprise architecture practice does not include:

Data quality management is a key capability of a data management practice and organization.

Data modeller: responsible for fata model version control an change control

Data architect: A senior analyst responsible for data architecture and data integration.

The neutral zone is one of the phases in the Bridges' transition phases.

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...

Change only requires change agents in special circumstances, especially when there is little to no adoption.

Please select the transition phases in Bridges' Transition process:

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:

Data lineage is useful to the development of the data governance strategy.

There are several reasons to denormalize dat

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

Effective data management involves a set of complex, interrelated processes that disable an organization to use its data to achieve strategic goals.

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

Temporal aspects usually include:

Hierarchical database model is the newest database model

Normalisation is the process of applying rules in order to organise business complexity into stable data structures.

Security Risks include elements that can compromise a network and/or database.

JSON is an open, lightweight standard format for data interchange.

Data quality rules and standards are a critical form of Metadat

Poorly managed metadata leads to: