WGU C175 OR D426 DATA
MANAGEMENT FOUNDATIONS OA
EXAM (2025) COMPLETE QUESTIONS
WITH 100% VERIFIED SOLUTIONS
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 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.
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.
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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.
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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.
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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.
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