Wgu C175 D426 Data Management Foundations Oa Exam Western Governors University Prep 6 Core Domains Questions And Correct Answers With Rationales| Instant Download

WGU C175 D426 DATA MANAGEMENT FOUNDATIONS OA EXAM 2026/2027
100 VERIFIED QUESTIONS & ANSWERS WESTERN GOVERNORS
UNIVERSITY PREP 6 CORE DOMAINS INSTANT DOWNLOAD
99 QUESTIONS




TABLE OF CONTENTS

# TOPIC

1 Analyze and design normalized relational schemas to eliminate redundancy and anomalies

2 Construct complex SQL queries involving joins, subqueries, and set operations

3 Evaluate transaction concurrency mechanisms and recovery techniques to ensure ACID properties

4 Compare and contrast data warehousing architectures and dimensional modeling approaches

5 Apply data integrity constraints and indexing strategies to optimize performance

6 WGU C175 D426 Data Management Foundations OA Exam 2026

7 2027 100 Verified Questions & Answers Western Governors University Prep 6 Core Domains Instant
Download

8 Foundations of Data Management Foundations

9 Applied Data Management Foundations

10 Advanced Data Management Foundations

11 Data Management Foundations Review


ABSTRACT

This study document brings together 99 carefully worded exam questions drawn from WGU C175
D426 Data Management Foundations OA Exam 2026/2027 100 Verified Questions & Answers
Western Governors University Prep 6 Core Domains Instant Download, with the strongest
emphasis placed on Analyze and design normalized relational schemas to eliminate redundancy
and anomalies, Construct complex SQL queries involving joins, subqueries, and set operations
and Evaluate transaction concurrency mechanisms and recovery techniques to ensure ACID
properties. Every item follows the wording style and level of reasoning you meet in the real paper,
and each one is paired with a clear rationale so the correct choice is never a guess. Work through
the set at your own pace, mark the questions that slow you down, then come back to them until the
reasoning feels automatic. Learners who revise this way walk into the exam room recognising the
pattern behind the questions instead of meeting them for the first time. Keep going - steady, honest
practice is what turns a difficult paper into a comfortable pass.




Page 1

,Q1 ANALYZE AND DESIGN NORMALIZED RELATIONAL SCHEMAS TO ELIMINATE
REDUNDANCY AND ANOMALIES
A relation R(A, B, C, D) has functional dependencies A->B, B->C, C->D. Which of
the following is a lossless-join, dependency-preserving 3NF decomposition of R?
A. R1(A, B), R2(B, C), R3(C, D) CORRECT

B. R1(A, B), R2(B, C, D)

C. R1(A, B), R2(A, C), R3(C, D)

D. R1(A, B, C), R2(C, D)

RATIONALE: The decomposition into R1(A,B), R2(B,C), R3(C,D) is lossless (common attributes
are keys) and preserves all FDs. Option B loses A->B? No, it preserves B->C and C->D but loses
A->B? Actually R2(B,C,D) preserves B->C, C->D, but A->B is lost. Option C is lossless but not
dependency-preserving because B->C is not preserved. Option D is lossless but loses A->B?
R1(A,B,C) preserves A->B, B->C; R2(C,D) preserves C->D, but the join of R1 and R2 is
lossless? Common attribute C is key of R2? C is not a key of R2? R2(C,D) has C as key only if
C->D, yes C is key, so lossless. But dependency preservation? A->B, B->C, C->D are all
preserved? A->B in R1, B->C in R1, C->D in R2, so it is dependency-preserving. However, the
question asks for a 3NF decomposition; option D is also 3NF? R1(A,B,C) has candidate key A,
and B,C are non-prime; but B->C is a transitive dependency? Actually A->B, B->C, so B is not a
superkey, so R1 is not in 3NF. Thus D is not 3NF. Option A is correct.




Q2 ANALYZE AND DESIGN NORMALIZED RELATIONAL SCHEMAS TO ELIMINATE
REDUNDANCY AND ANOMALIES
In a database using multiversion concurrency control (MVCC), a transaction T1
reads a data item X that was written by an uncommitted transaction T2. Which
isolation level is most likely in effect?
A. READ UNCOMMITTED CORRECT

B. READ COMMITTED

C. REPEATABLE READ

D. SERIALIZABLE

RATIONALE: READ UNCOMMITTED allows dirty reads, so T1 can read uncommitted data from
T2. READ COMMITTED prevents dirty reads by only allowing reads of committed data.
REPEATABLE READ and SERIALIZABLE also prevent dirty reads. Thus, the scenario indicates
READ UNCOMMITTED.




Page 2

,Q3 ANALYZE AND DESIGN NORMALIZED RELATIONAL SCHEMAS TO ELIMINATE
REDUNDANCY AND ANOMALIES
Which of the following SQL statements correctly retrieves the names of customers
who have placed more than 5 orders, using a correlated subquery?
A. SELECT c.name FROM Customers c WHERE (SELECT COUNT(*) FROM Orders o WHERE
o.customer_id = c.id) > 5; CORRECT

B. SELECT c.name FROM Customers c WHERE COUNT(SELECT * FROM Orders o WHERE
o.customer_id = c.id) > 5;

C. SELECT c.name FROM Customers c JOIN Orders o ON c.id = o.customer_id GROUP BY
c.name HAVING COUNT(*) > 5;

D. SELECT c.name FROM Customers c WHERE EXISTS (SELECT * FROM Orders o WHERE
o.customer_id = c.id AND COUNT(*) > 5);

RATIONALE: Option A uses a correlated subquery to count orders per customer and filters those
with more than 5. Option B is syntactically invalid. Option C uses a join and GROUP BY, which is
not a correlated subquery. Option D uses EXISTS with an aggregate, which is invalid. Thus, A is
correct.




Q4 ANALYZE AND DESIGN NORMALIZED RELATIONAL SCHEMAS TO ELIMINATE
REDUNDANCY AND ANOMALIES
In a star schema, which of the following best describes the role of a degenerate
dimension?
A. A dimension key that is stored in the fact table but has no corresponding dimension table.
CORRECT

B. A dimension that is snowflaked into multiple tables.

C. A dimension that contains only one attribute.

D. A dimension that is shared across multiple fact tables.

RATIONALE: A degenerate dimension is a dimension key (like an invoice number) that remains
in the fact table without its own dimension table. It is not snowflaked, not necessarily
single-attribute, and not about sharing across fact tables. Thus, A is correct.




Page 3

, Q5 ANALYZE AND DESIGN NORMALIZED RELATIONAL SCHEMAS TO ELIMINATE
REDUNDANCY AND ANOMALIES
A database administrator needs to ensure that a transaction's updates are durable
even if the system crashes immediately after commit. Which mechanism is
primarily responsible for this?
A. Write-ahead logging (WAL) CORRECT

B. Two-phase locking (2PL)

C. Shadow paging

D. Checkpointing

RATIONALE: Write-ahead logging ensures durability by forcing log records to stable storage
before commit. 2PL ensures serializability, shadow paging is an alternative recovery technique
but less common, and checkpointing reduces recovery time but does not by itself guarantee
durability. Thus, A is correct.




Q6 ANALYZE AND DESIGN NORMALIZED RELATIONAL SCHEMAS TO ELIMINATE
REDUNDANCY AND ANOMALIES
Which of the following is a key difference between a data lake and a data
warehouse?
A. Data lakes store raw data in its native format, while data warehouses store processed,
structured data. CORRECT

B. Data lakes enforce schema-on-write, while data warehouses use schema-on-read.

C. Data warehouses are typically built on Hadoop, while data lakes use relational databases.

D. Data lakes are optimized for ACID transactions, while data warehouses are not.

RATIONALE: Data lakes store raw data in native format (schema-on-read), while data
warehouses store processed, structured data (schema-on-write). Option B reverses the schema
enforcement. Option C is incorrect as data lakes often use Hadoop. Option D is false; data
warehouses are optimized for ACID, not data lakes. Thus, A is correct.




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