WGU C175/D426 Data Management
Foundations OA Question and Answer |
Latest Update Complete Exam Prep with
Accurate Solutions | Grade A+
• Define: Referential Integrity -✓✓Requires that ALL foreign key values must
either be fully NULL or match some primary key value.
• 4 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 Violation -✓✓1. RESTRICT - rejects an
insert, update, or delete 2. SET NULL - 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.
• What is an Important aspect of Referential Integrity -✓✓Reference to data in one
relation is based on values in another relation.
• What is Broad definition of data -✓✓Raw facts captured on printed or digital
media.
• What Data -✓✓Facts that are collected and stored in a database system.
• Determining characteristic of unstructured data -✓✓It does not follow a data
model.
,• Flat files -✓✓They contain no internal hierarchical organization.
• Data retrieval before database management systems -✓✓Sequentially from
simple files.
• 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 analytical databases -✓✓- Volatility - Detail
- Scope - History.
• 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 aggregated -✓✓Transformation steps.
• 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.
Foundations OA Question and Answer |
Latest Update Complete Exam Prep with
Accurate Solutions | Grade A+
• Define: Referential Integrity -✓✓Requires that ALL foreign key values must
either be fully NULL or match some primary key value.
• 4 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 Violation -✓✓1. RESTRICT - rejects an
insert, update, or delete 2. SET NULL - 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.
• What is an Important aspect of Referential Integrity -✓✓Reference to data in one
relation is based on values in another relation.
• What is Broad definition of data -✓✓Raw facts captured on printed or digital
media.
• What Data -✓✓Facts that are collected and stored in a database system.
• Determining characteristic of unstructured data -✓✓It does not follow a data
model.
,• Flat files -✓✓They contain no internal hierarchical organization.
• Data retrieval before database management systems -✓✓Sequentially from
simple files.
• 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 analytical databases -✓✓- Volatility - Detail
- Scope - History.
• 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 aggregated -✓✓Transformation steps.
• 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.
