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WGU C175/D426 Data Management Exam Review with 100% solution
Terms in this set (348)
Modality Refers to the MINIMUM number of times an instance in one
entity can be associated with instance of another entity
(minima). Appears as a 0 or 1 on the relationship line, next to
cardinality.
Referential Integrity Requires that ALL foreign key values must either be fully
NULL or match some primary key value.
Ways Referential Integrity can be violated 1. Primary key is updated 2. Foreign key is updated 3. Row
containing primary key is DELETED 4. Row containing foreign
key is INSERTED.
Actions to Correct Referential Integrity 1. RESTRICT - rejects an insert, update, or delete 2. SET NULL -
Violation sets invalid foreign keys to null 3. SET DEFAULT - sets
invalid foreign keys to a default primary value 4.
CASCADE -
propagates primary key changes to foreign keys.
,Important aspect of Referential Integrity Reference to data in one relation is based on values in another
relation.
Broad definition of data Raw facts captured on printed or digital media.
Data Facts that are collected and stored in a database system.
Determining characteristic of unstructured It does not follow a data model.
data
Flat files They contain no internal hierarchical organization.
Data retrieval before database management Sequentially from simple files.
systems
Primary Key An attribute or group of attributes that uniquely identify a
tuple in a relation.
Foreign Key matching A domain of values is necessary for a primary key in one
relation of a database to match with its corresponding foreign
key in another relation of the same database.
Alternate Key What uniquely identifies each entity in a collection of
entities but is not the primary key.
Candidate Key A set of columns in a table that can uniquely identify any
record in that table without referring to other data.
Database indexing The original data is copied to the index.
Indexes in physical database design To retrieve data DIRECTLY using a pointer.
Index creation on a database column To optimize data retrievals.
Functional Dependency Each value of a column relates to at MOST one value of
another column.
Rules/Appearance of First Normal Form - All non-key columns depend on primary key - Each table
cell contains one value - A table with no duplicate rows.
Rules/Appearance of Second Normal Form - When all non-key columns depend on the WHOLE primary
key - Must be in 1NF - Non-key column can not depend
on just one part of a composite key - a single primary
key is
automatically in 2NF.
Rules/Appearance of Third Normal Form - All non-key columns depend ONLY on the primary key -
Tables are totally free of data redundancy.
Differences between operational and - Volatility - Detail - Scope - History.
analytical databases
, Volatility Database updates in real time. Operational Data is
Volatile. Analytical Data is NOT Volatile.
Detail in databases - A database that keeps record of individual transactions;
line items - Operational: Detailed - Analytical: Detailed.
Scope in databases - How far a database can reach - Operational: incompatible -
Analytical: Enterprise-Wide/Summary.
History in databases - Whether DB is current or tracks all data - Operational:
Current only - Analytical: Tracks trends.
Data warehouse refresh process 1. Extraction 2. Cleanse 3. Integrate 4. Restructure 5. Load.
Extraction in ETL Data extracted and put into staging area.
Cleanse in ETL Errors are eliminated from data; standard abbreviations
applied.
Integrate in ETL Data is put into a uniform structure; Data converted to uniform
structure.
Restructure in ETL Data is structured in a design that is optimal for analysis.
Load in ETL Data is loaded to the data warehouse.
Issue focused on 'Load' component of ETL Monitor refreshing volume and frequency.
Step in ETL Process where raw data is Transformation steps.
aggregated
Data mining activities 1. Clustering & Segmentation 2. Classification 3. Estimation 4.
Prediction 5. Affinity Grouping 6. Description.
Clustering & Segmentation Taking large entity and dividing into smaller groups of
entities. Useful when unsure of what looking for.
Classification (Data Mining) Organizing data into predefined classes.
Estimation (Data Mining) Assigning a numeric value to an object.
Prediction (Data Mining) Classifying objects according to an expected future behavior.
Affinity Grouping Evaluating relationships between data elements that
demonstrate some kind of affinity between objects.
Entity types The uniquely identifiable element about which data can be
categorized in an entity-relationship diagram.
WGU C175/D426 Data Management Exam Review with 100% solution
Terms in this set (348)
Modality Refers to the MINIMUM number of times an instance in one
entity can be associated with instance of another entity
(minima). Appears as a 0 or 1 on the relationship line, next to
cardinality.
Referential Integrity Requires that ALL foreign key values must either be fully
NULL or match some primary key value.
Ways Referential Integrity can be violated 1. Primary key is updated 2. Foreign key is updated 3. Row
containing primary key is DELETED 4. Row containing foreign
key is INSERTED.
Actions to Correct Referential Integrity 1. RESTRICT - rejects an insert, update, or delete 2. SET NULL -
Violation sets invalid foreign keys to null 3. SET DEFAULT - sets
invalid foreign keys to a default primary value 4.
CASCADE -
propagates primary key changes to foreign keys.
,Important aspect of Referential Integrity Reference to data in one relation is based on values in another
relation.
Broad definition of data Raw facts captured on printed or digital media.
Data Facts that are collected and stored in a database system.
Determining characteristic of unstructured It does not follow a data model.
data
Flat files They contain no internal hierarchical organization.
Data retrieval before database management Sequentially from simple files.
systems
Primary Key An attribute or group of attributes that uniquely identify a
tuple in a relation.
Foreign Key matching A domain of values is necessary for a primary key in one
relation of a database to match with its corresponding foreign
key in another relation of the same database.
Alternate Key What uniquely identifies each entity in a collection of
entities but is not the primary key.
Candidate Key A set of columns in a table that can uniquely identify any
record in that table without referring to other data.
Database indexing The original data is copied to the index.
Indexes in physical database design To retrieve data DIRECTLY using a pointer.
Index creation on a database column To optimize data retrievals.
Functional Dependency Each value of a column relates to at MOST one value of
another column.
Rules/Appearance of First Normal Form - All non-key columns depend on primary key - Each table
cell contains one value - A table with no duplicate rows.
Rules/Appearance of Second Normal Form - When all non-key columns depend on the WHOLE primary
key - Must be in 1NF - Non-key column can not depend
on just one part of a composite key - a single primary
key is
automatically in 2NF.
Rules/Appearance of Third Normal Form - All non-key columns depend ONLY on the primary key -
Tables are totally free of data redundancy.
Differences between operational and - Volatility - Detail - Scope - History.
analytical databases
, Volatility Database updates in real time. Operational Data is
Volatile. Analytical Data is NOT Volatile.
Detail in databases - A database that keeps record of individual transactions;
line items - Operational: Detailed - Analytical: Detailed.
Scope in databases - How far a database can reach - Operational: incompatible -
Analytical: Enterprise-Wide/Summary.
History in databases - Whether DB is current or tracks all data - Operational:
Current only - Analytical: Tracks trends.
Data warehouse refresh process 1. Extraction 2. Cleanse 3. Integrate 4. Restructure 5. Load.
Extraction in ETL Data extracted and put into staging area.
Cleanse in ETL Errors are eliminated from data; standard abbreviations
applied.
Integrate in ETL Data is put into a uniform structure; Data converted to uniform
structure.
Restructure in ETL Data is structured in a design that is optimal for analysis.
Load in ETL Data is loaded to the data warehouse.
Issue focused on 'Load' component of ETL Monitor refreshing volume and frequency.
Step in ETL Process where raw data is Transformation steps.
aggregated
Data mining activities 1. Clustering & Segmentation 2. Classification 3. Estimation 4.
Prediction 5. Affinity Grouping 6. Description.
Clustering & Segmentation Taking large entity and dividing into smaller groups of
entities. Useful when unsure of what looking for.
Classification (Data Mining) Organizing data into predefined classes.
Estimation (Data Mining) Assigning a numeric value to an object.
Prediction (Data Mining) Classifying objects according to an expected future behavior.
Affinity Grouping Evaluating relationships between data elements that
demonstrate some kind of affinity between objects.
Entity types The uniquely identifiable element about which data can be
categorized in an entity-relationship diagram.
