WGU D426 V3 DATA MANAGEMENT FOUNDATIONS OA
MASTER STUDY GUIDE | SQL, DATABASE DESIGN, ERD,
KEYS & NORMALIZATION | 2026 UPDATED.
90 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
WGU D426 V3 DATA MANAGEMENT FOUNDATIONS OA MASTER STUDY GUIDE | SQL, DATABASE
DESIGN, ERD, KEYS & NORMALIZATION | 2026 UPDATED.. It contains 90 carefully selected questions that
reflect the most current exam content and testing strategies. Each question is accompanied by a correct answer
and a detailed rationale that explains the underlying pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 90 Questions
Foundations - Application - WGU D426 V3 DATA Management Foundations OA Master Study Guide SQL
Database Design ERD KEYS & Normalization 2026 Updated DATA Management Foundations SQL Database
Design ERD KEYS & Normalization Undergraduate YEAR 3 / Advanced
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Database Fundamentals AND 1-15 Dependencies, Correctly, Query, Entity, Reads
Architecture
SQL DATA Definition 16-30 Table, Relational, Database, Employee, Orders
Language DDL AND DATA
Manipulation Language DML
SQL Queries AND DATA 31-45 Relation, Functional Dependencies, Entity, Column, Database
Retrieval
Database Design AND ERD 46-60 Table, Relational Database, Correctly, Valid, SQL Statements
Modeling
KEYS AND Relationships 61-75 Dependencies, Functional, Reads, Database, Attribute
Normalization AND 76-90 Table, Database, Relational, Functional, Column
Denormalization
TOTAL 90 All questions include answers and detailed rationales
,Section A - Database Fundamentals AND Architecture
Q1.
Given the functional dependencies A -> B, B -> C, and C -> A, which of the following is a
valid lossless join decomposition of relation R(A,B,C) into 3NF while preserving all
dependencies?
A. R1(A,B), R2(B,C) B. R1(A,B), R2(A,C)
C. R1(A,B,C) D. R1(A,B), R2(B,C), R3(C,A)
Correct: C - R1(A,B,C)
Rationale:Since A!’B, B!’C, and C!’A form a cycle, all attributes are prime; the relation is
already in 3NF (and BCNF). A single relation R1(A,B,C) is a lossless, dependency-preserving
decomposition. Other decompositions lose the cycle or are not minimal.
Why the other answers are wrong:
A. This decomposition loses the dependency C->A.
B. This decomposition loses the dependency B->C.
D. This is redundant and not a minimal decomposition.
Reference: Elmasri & Navathe, Fundamentals of Database Systems, 7th Ed., Ch. 15
Q2.
Which of the following SQL statements correctly implements a query to list departments
whose average employee salary exceeds the overall average salary, using a subquery?
A. SELECT dept_id FROM employees B. SELECT dept_id FROM employees
GROUP BY dept_id HAVING AVG(salary) > WHERE AVG(salary) > (SELECT
(SELECT AVG(salary) FROM employees); AVG(salary) FROM employees) GROUP BY
dept_id;
C. SELECT dept_id FROM employees D. SELECT dept_id FROM employees
GROUP BY dept_id WHERE AVG(salary) > HAVING AVG(salary) > (SELECT
(SELECT AVG(salary) FROM employees); AVG(salary) FROM employees) GROUP BY
dept_id;
Correct: A - SELECT dept_id FROM employees GROUP BY dept_id HAVING AVG(salary) >
(SELECT AVG(salary) FROM employees);
Rationale:The HAVING clause is used to filter groups after aggregation, and the subquery
computes the overall average. Option A correctly groups by dept_id and applies the condition
in HAVING.
Why the other answers are wrong:
B. WHERE cannot contain aggregate functions.
C. WHERE cannot be used after GROUP BY.
Page 3
, Section A - Database Fundamentals AND Architecture
D. HAVING must come after GROUP BY.
Reference: Elmasri & Navathe, Ch. 7
Q3.
In an entity-relationship model, a weak entity type is one that lacks its own key attributes.
Which of the following is a mandatory condition for the existence of a weak entity?
A. It must participate in a total participation B. It must have a partial key that uniquely
relationship with an identifying owner entity. identifies it within the entire database.
C. It must be connected to at least two D. It must have a single-valued attribute that
strong entity types via separate can serve as a primary key after adding the
relationships. owner's key.
Correct: A - It must participate in a total participation relationship with an identifying
owner entity.
Rationale:A weak entity depends on an identifying owner via a total participation relationship
(existence dependency). Its discriminator (partial key) only uniquely identifies it within the
owner's set.
Why the other answers are wrong:
B. A partial key is not unique globally; it is only unique relative to the owner.
C. A weak entity typically has one identifying owner, not two.
D. The weak entity's key is derived from the owner's key plus its partial key, but the condition is
total participation.
Reference: Elmasri & Navathe, Ch. 7
Q4.
Which of the following scenarios would cause an index to be ineffective for speeding up a
query?
A. A composite index on (last_name, B. A B-tree index on an integer column and
first_name) and a query filtering on a query using an equality condition on that
first_name alone. column.
C. A bitmap index on a low-cardinality D. A covering index that includes all
column and a query using a range condition. columns needed by the query.
Correct: A - A composite index on (last_name, first_name) and a query filtering on
first_name alone.
Rationale:In a composite index, the leading column is most important; querying on a
non-leading column (first_name) typically cannot use the index efficiently. Range conditions
on low-cardinality bitmap indexes can be effective, and covering indexes are designed to be
effective.
Why the other answers are wrong:
Page 4
MASTER STUDY GUIDE | SQL, DATABASE DESIGN, ERD,
KEYS & NORMALIZATION | 2026 UPDATED.
90 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
WGU D426 V3 DATA MANAGEMENT FOUNDATIONS OA MASTER STUDY GUIDE | SQL, DATABASE
DESIGN, ERD, KEYS & NORMALIZATION | 2026 UPDATED.. It contains 90 carefully selected questions that
reflect the most current exam content and testing strategies. Each question is accompanied by a correct answer
and a detailed rationale that explains the underlying pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 90 Questions
Foundations - Application - WGU D426 V3 DATA Management Foundations OA Master Study Guide SQL
Database Design ERD KEYS & Normalization 2026 Updated DATA Management Foundations SQL Database
Design ERD KEYS & Normalization Undergraduate YEAR 3 / Advanced
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Database Fundamentals AND 1-15 Dependencies, Correctly, Query, Entity, Reads
Architecture
SQL DATA Definition 16-30 Table, Relational, Database, Employee, Orders
Language DDL AND DATA
Manipulation Language DML
SQL Queries AND DATA 31-45 Relation, Functional Dependencies, Entity, Column, Database
Retrieval
Database Design AND ERD 46-60 Table, Relational Database, Correctly, Valid, SQL Statements
Modeling
KEYS AND Relationships 61-75 Dependencies, Functional, Reads, Database, Attribute
Normalization AND 76-90 Table, Database, Relational, Functional, Column
Denormalization
TOTAL 90 All questions include answers and detailed rationales
,Section A - Database Fundamentals AND Architecture
Q1.
Given the functional dependencies A -> B, B -> C, and C -> A, which of the following is a
valid lossless join decomposition of relation R(A,B,C) into 3NF while preserving all
dependencies?
A. R1(A,B), R2(B,C) B. R1(A,B), R2(A,C)
C. R1(A,B,C) D. R1(A,B), R2(B,C), R3(C,A)
Correct: C - R1(A,B,C)
Rationale:Since A!’B, B!’C, and C!’A form a cycle, all attributes are prime; the relation is
already in 3NF (and BCNF). A single relation R1(A,B,C) is a lossless, dependency-preserving
decomposition. Other decompositions lose the cycle or are not minimal.
Why the other answers are wrong:
A. This decomposition loses the dependency C->A.
B. This decomposition loses the dependency B->C.
D. This is redundant and not a minimal decomposition.
Reference: Elmasri & Navathe, Fundamentals of Database Systems, 7th Ed., Ch. 15
Q2.
Which of the following SQL statements correctly implements a query to list departments
whose average employee salary exceeds the overall average salary, using a subquery?
A. SELECT dept_id FROM employees B. SELECT dept_id FROM employees
GROUP BY dept_id HAVING AVG(salary) > WHERE AVG(salary) > (SELECT
(SELECT AVG(salary) FROM employees); AVG(salary) FROM employees) GROUP BY
dept_id;
C. SELECT dept_id FROM employees D. SELECT dept_id FROM employees
GROUP BY dept_id WHERE AVG(salary) > HAVING AVG(salary) > (SELECT
(SELECT AVG(salary) FROM employees); AVG(salary) FROM employees) GROUP BY
dept_id;
Correct: A - SELECT dept_id FROM employees GROUP BY dept_id HAVING AVG(salary) >
(SELECT AVG(salary) FROM employees);
Rationale:The HAVING clause is used to filter groups after aggregation, and the subquery
computes the overall average. Option A correctly groups by dept_id and applies the condition
in HAVING.
Why the other answers are wrong:
B. WHERE cannot contain aggregate functions.
C. WHERE cannot be used after GROUP BY.
Page 3
, Section A - Database Fundamentals AND Architecture
D. HAVING must come after GROUP BY.
Reference: Elmasri & Navathe, Ch. 7
Q3.
In an entity-relationship model, a weak entity type is one that lacks its own key attributes.
Which of the following is a mandatory condition for the existence of a weak entity?
A. It must participate in a total participation B. It must have a partial key that uniquely
relationship with an identifying owner entity. identifies it within the entire database.
C. It must be connected to at least two D. It must have a single-valued attribute that
strong entity types via separate can serve as a primary key after adding the
relationships. owner's key.
Correct: A - It must participate in a total participation relationship with an identifying
owner entity.
Rationale:A weak entity depends on an identifying owner via a total participation relationship
(existence dependency). Its discriminator (partial key) only uniquely identifies it within the
owner's set.
Why the other answers are wrong:
B. A partial key is not unique globally; it is only unique relative to the owner.
C. A weak entity typically has one identifying owner, not two.
D. The weak entity's key is derived from the owner's key plus its partial key, but the condition is
total participation.
Reference: Elmasri & Navathe, Ch. 7
Q4.
Which of the following scenarios would cause an index to be ineffective for speeding up a
query?
A. A composite index on (last_name, B. A B-tree index on an integer column and
first_name) and a query filtering on a query using an equality condition on that
first_name alone. column.
C. A bitmap index on a low-cardinality D. A covering index that includes all
column and a query using a range condition. columns needed by the query.
Correct: A - A composite index on (last_name, first_name) and a query filtering on
first_name alone.
Rationale:In a composite index, the leading column is most important; querying on a
non-leading column (first_name) typically cannot use the index efficiently. Range conditions
on low-cardinality bitmap indexes can be effective, and covering indexes are designed to be
effective.
Why the other answers are wrong:
Page 4
