WGU C175/D426 Data Management Foundations OA
Exam Questions and Answers (2026/2027) | Latest
Exam Practice Questions | Fully Verified Answers | 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.
• 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.
• Referential integrity rules by modern relational database management systems -
✓✓Insert, Update, Delete.
• DISTINCT clause -✓✓Returns only unique or 'distinct' values; Filters Data Results.
• ORDER BY clause -✓✓Modifies presentation of data results.
• Heap file -✓✓A file where records can be placed anywhere in the memory.
• Hash file -✓✓A file that uses Hash function computation on some fields of the records,
and the result of that computation determines where the record is stored.
• Major Joins -✓✓- LEFT JOIN - RIGHT JOIN - INNER JOIN - FULL JOIN.
Exam Questions and Answers (2026/2027) | Latest
Exam Practice Questions | Fully Verified Answers | 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.
• 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.
• Referential integrity rules by modern relational database management systems -
✓✓Insert, Update, Delete.
• DISTINCT clause -✓✓Returns only unique or 'distinct' values; Filters Data Results.
• ORDER BY clause -✓✓Modifies presentation of data results.
• Heap file -✓✓A file where records can be placed anywhere in the memory.
• Hash file -✓✓A file that uses Hash function computation on some fields of the records,
and the result of that computation determines where the record is stored.
• Major Joins -✓✓- LEFT JOIN - RIGHT JOIN - INNER JOIN - FULL JOIN.
