WGU C175 DATA MANAGEMENT FOUNDATIONS OA
LATEST EXAM 2024/2025 QUESTIONS AND VERIFIED
CORRECT ANSWERS/ALREADY GRADED A++
slice and dice - ANSWER The ability to focus on slices of a data cube (drill
down or roll up) to perform a more detailed analysis.
sparsity - ANSWER In multidimensional data analysis, a measurement of
the data density held in the data cube.
very large databases (VLDBs) - ANSWER Database that contains huge
amounts of data—gigabyte, terabyte, and petabyte ranges are not unusual.
Clustering - ANSWER It is the task of taking a large collection of entities
and dividing that collection into smaller groups of entities that exhibit some
similarity.
Estimation - ANSWER It is a process of assigning some continuously
valued numeric value to an object.
Affinity grouping - ANSWER is a process of evaluating relationships or
associations between data elements that demonstrate some kind of affinity
between objects.
Description - ANSWER It is the process of trying to characterize what has
been discovered or trying to explain the results of the data mining process.
Prediction - ANSWER The subtle difference between prediction and the
previous two tasks is that prediction is the attempt to classify objects
according to some expected future behavior. Classification and estimation
can be used for the purposes of prediction by using historical data, where
the classification is already known, to build a model (this is called training).
That model can then be applied to new data to predict future behavior.
You must be careful when using training sets for prediction. There may be
a risk of an inherent bias in the data that may lead you to draw inferences
or conclusions that are relevant in relation to the bias. Use different data
sets for training and test, test, test!
,The difference between clustering and classification - ANSWER is that
during the clustering task, the classes are not defined beforehand.
ETL Stages - Extraction - ANSWER What data should be extracted?
How should that data be extracted?
ETL Stages - Transformation - ANSWER Data type conversion
Data cleansing.
Integration.
Referential integrity checking.
Derivations
Denormalization and renormalization.
Aggregation
Audit information.
Null conversion.
ETL Stages - Loading - ANSWER Target dependencies
Refresh volume and frequency
Fundamental aspects of a data warehouse - ANSWER A data warehouse
is a centralized repository of information.
A data warehouse is organized around the relevant subject areas
important to the organization.
A data warehouse provides a platform for different consumers (both
human and automated) to submit queries about enterprise information.
A data warehouse is used for analysis and not for transaction processing.
The data in a data warehouse is nonvolatile.
A data warehouse is the target location for integrating data from multiple
sources, both internal and external to an enterprise.
data warehouse - ANSWER is the primary source of information that feeds
the analytical processing within an organization.
Using the Dimensional Model for Business Intelligence - ANSWER
Simplicity
Lack of bias.
Extensibility
,dimensional model - ANSWER standard for representing and managing
data in a data warehouse
OLAP cube - ANSWER environment provides an aggregate view of data
variables across the dimensions across each dimension's hierarchy.
Why a Business Intelligence Program? - ANSWER Financial value
associated with increased profitability, whether derived from lowered costs
or increased revenues;
Productivity value associated with increased throughput with decreased
workloads, diminished time for executing end-to-end processes (such as
manufacturing or
operational workflows), and increasing the percentage of high quality
products or outcomes;
Trust value, such as greater customer, employee, or supplier satisfaction,
as well as increasing confidence in forecasting, maintaining consistent
operational and
management reports, reductions in time spent in "analysis paralysis," and
better results from decisions; and
Risk value associated with improved visibility into credit exposure,
confidence in
capital and asset investments, and auditable compliance with jurisdictional
and industry standards and regulations.
Data - ANSWER Facts gathered together for analysis
Flat Files - ANSWER A file having no internal hierarchy
Hashed Files - ANSWER A file that has been encrypted for security
purposes
Heap Files - ANSWER An unsorted set of records
Information - ANSWER The transformation of raw data into useful facts
Structured Data - ANSWER Information with a high degree of organization
, Punch Card - ANSWER A card that is perforated and can hold commands
or data
Unstructured Data - ANSWER Information that does not have structure
(such as text)
data retrieved before database management systems - ANSWER
Sequentially from simple files
unique identifier - ANSWER primary key
intersection data - ANSWER describes the relationship between the two
entities(Quantity)
entity - ANSWER is an object or event in our environment that we want to
keep track of. A person is an entity. So is a building, a piece of inventory
sitting on a shelf, a finished product ready for sale, and a sales meeting (an
event).
attribute - ANSWER is a property or characteristic of an entity. Examples of
attributes include an employee's employee number, the weight of an
automobile, a company's address, or the date of a sales meeting.
modalities - ANSWER minimums
cardinalities - ANSWER maximums
referential integrity - ANSWER problem because it revolves around the
circumstance of trying to refer to data in one relation in the database,
based on values in another relation.
referential integrity problems can surface in any of the three operations -
ANSWER that result in changes to the database—insert, delete, and
update records
factors that lead to redundant data across multiple files: - ANSWER Data
was stored in different formats in different files.
Data was often not shared among different programs that needed it,
necessitating the duplication of data in redundant files.
LATEST EXAM 2024/2025 QUESTIONS AND VERIFIED
CORRECT ANSWERS/ALREADY GRADED A++
slice and dice - ANSWER The ability to focus on slices of a data cube (drill
down or roll up) to perform a more detailed analysis.
sparsity - ANSWER In multidimensional data analysis, a measurement of
the data density held in the data cube.
very large databases (VLDBs) - ANSWER Database that contains huge
amounts of data—gigabyte, terabyte, and petabyte ranges are not unusual.
Clustering - ANSWER It is the task of taking a large collection of entities
and dividing that collection into smaller groups of entities that exhibit some
similarity.
Estimation - ANSWER It is a process of assigning some continuously
valued numeric value to an object.
Affinity grouping - ANSWER is a process of evaluating relationships or
associations between data elements that demonstrate some kind of affinity
between objects.
Description - ANSWER It is the process of trying to characterize what has
been discovered or trying to explain the results of the data mining process.
Prediction - ANSWER The subtle difference between prediction and the
previous two tasks is that prediction is the attempt to classify objects
according to some expected future behavior. Classification and estimation
can be used for the purposes of prediction by using historical data, where
the classification is already known, to build a model (this is called training).
That model can then be applied to new data to predict future behavior.
You must be careful when using training sets for prediction. There may be
a risk of an inherent bias in the data that may lead you to draw inferences
or conclusions that are relevant in relation to the bias. Use different data
sets for training and test, test, test!
,The difference between clustering and classification - ANSWER is that
during the clustering task, the classes are not defined beforehand.
ETL Stages - Extraction - ANSWER What data should be extracted?
How should that data be extracted?
ETL Stages - Transformation - ANSWER Data type conversion
Data cleansing.
Integration.
Referential integrity checking.
Derivations
Denormalization and renormalization.
Aggregation
Audit information.
Null conversion.
ETL Stages - Loading - ANSWER Target dependencies
Refresh volume and frequency
Fundamental aspects of a data warehouse - ANSWER A data warehouse
is a centralized repository of information.
A data warehouse is organized around the relevant subject areas
important to the organization.
A data warehouse provides a platform for different consumers (both
human and automated) to submit queries about enterprise information.
A data warehouse is used for analysis and not for transaction processing.
The data in a data warehouse is nonvolatile.
A data warehouse is the target location for integrating data from multiple
sources, both internal and external to an enterprise.
data warehouse - ANSWER is the primary source of information that feeds
the analytical processing within an organization.
Using the Dimensional Model for Business Intelligence - ANSWER
Simplicity
Lack of bias.
Extensibility
,dimensional model - ANSWER standard for representing and managing
data in a data warehouse
OLAP cube - ANSWER environment provides an aggregate view of data
variables across the dimensions across each dimension's hierarchy.
Why a Business Intelligence Program? - ANSWER Financial value
associated with increased profitability, whether derived from lowered costs
or increased revenues;
Productivity value associated with increased throughput with decreased
workloads, diminished time for executing end-to-end processes (such as
manufacturing or
operational workflows), and increasing the percentage of high quality
products or outcomes;
Trust value, such as greater customer, employee, or supplier satisfaction,
as well as increasing confidence in forecasting, maintaining consistent
operational and
management reports, reductions in time spent in "analysis paralysis," and
better results from decisions; and
Risk value associated with improved visibility into credit exposure,
confidence in
capital and asset investments, and auditable compliance with jurisdictional
and industry standards and regulations.
Data - ANSWER Facts gathered together for analysis
Flat Files - ANSWER A file having no internal hierarchy
Hashed Files - ANSWER A file that has been encrypted for security
purposes
Heap Files - ANSWER An unsorted set of records
Information - ANSWER The transformation of raw data into useful facts
Structured Data - ANSWER Information with a high degree of organization
, Punch Card - ANSWER A card that is perforated and can hold commands
or data
Unstructured Data - ANSWER Information that does not have structure
(such as text)
data retrieved before database management systems - ANSWER
Sequentially from simple files
unique identifier - ANSWER primary key
intersection data - ANSWER describes the relationship between the two
entities(Quantity)
entity - ANSWER is an object or event in our environment that we want to
keep track of. A person is an entity. So is a building, a piece of inventory
sitting on a shelf, a finished product ready for sale, and a sales meeting (an
event).
attribute - ANSWER is a property or characteristic of an entity. Examples of
attributes include an employee's employee number, the weight of an
automobile, a company's address, or the date of a sales meeting.
modalities - ANSWER minimums
cardinalities - ANSWER maximums
referential integrity - ANSWER problem because it revolves around the
circumstance of trying to refer to data in one relation in the database,
based on values in another relation.
referential integrity problems can surface in any of the three operations -
ANSWER that result in changes to the database—insert, delete, and
update records
factors that lead to redundant data across multiple files: - ANSWER Data
was stored in different formats in different files.
Data was often not shared among different programs that needed it,
necessitating the duplication of data in redundant files.
