Page 1 of 117
WGU D684 Introduction to Computer Science OA Exam
COMPLETE QUESTIONS AND DETAILED SOLUTIONS
LATEST UPDATE THIS YEAR JUST RELEASED
WGU D684 Introduction to Computer Science OA Exam — Detailed Coverage Guide
The WGU D684 Introduction to Computer Science OA (Objective Assessment) is a theory-heavy
foundational computer science exam that evaluates understanding of computer systems, algorithms,
programming logic, operating systems, networking, data structures, SDLC, and computational
thinking. Students consistently report that the OA closely aligns with the PA but is slightly more
scenario-based and terminology-focused.
1. Computational Thinking & Problem Solving
• Decomposition of complex problems
• Pattern recognition concepts
• Abstraction and generalization
• Algorithmic thinking and logical sequencing
• Step-by-step problem-solving process
• Computational models and automation basics
2. Computer Architecture Fundamentals
• Von Neumann architecture
• CPU components (ALU, Control Unit, Registers)
• Memory Unit functions
• Instruction Register vs Program Counter
• Fetch-decode-execute cycle
• Input/output devices and buses
• Multi-core processors and parallel processing
• Cache memory basics
3. Data Representation & Variables
• Binary number system basics
• Bits, bytes, and data encoding
• Variables and constants
• Primitive data types (int, float, Boolean, char, string)
• Strong typing concepts
• Type conversion and casting
• Assignment operations
4. Programming Logic & Pseudocode
• If/then and if/then/else logic
• Nested conditionals
• For loops and while loops
• Count-controlled vs event-controlled loops
• Functions and subprograms
• Parameters vs arguments
, Page 2 of 117
• Pseudocode tracing and interpretation
• Selecting correct logic for given scenarios
• Basic debugging concepts
Students report pseudocode interpretation is one of the most tested OA areas.
5. Algorithms & Complexity
• Definition and purpose of algorithms
• Linear search vs binary search
• Bubble sort, insertion sort, selection sort
• Basic algorithm efficiency concepts
• Sequential vs divide-and-conquer methods
• Complexity awareness (constant, linear, quadratic)
• Choosing the correct algorithm for scenarios
f(n)=n2f(n)=n^2f(n)=n2
6. Data Structures & Abstract Data Types
• Arrays and lists
• Linked lists
• Stacks (LIFO behavior)
• Queues (FIFO behavior)
• Abstract Data Types (ADT)
• Composite variables
• Data organization and retrieval methods
Students frequently mention stack vs queue questions on the OA.
7. Operating Systems
• Functions of an operating system
• Resource management responsibilities
• Process management fundamentals
• Process states: ready, running, waiting, terminated
• Process Control Block (PCB)
• Memory management techniques
• Fixed vs dynamic partitioning
• Virtual memory basics
• Paging and fragmentation
• File systems and OS interaction
Memory management is repeatedly identified as a major OA topic.
8. File Systems & Storage
• File organization principles
• Directories and folder structures
• Root directory concepts
• Absolute path vs relative path
• File allocation methods
• Storage devices and persistence
• Volatile vs non-volatile memory
9. Networking Fundamentals
, Page 3 of 117
• LAN, MAN, and WAN
• Ring, bus, and star topologies
• DNS and TLD concepts
• Routers, switches, and servers
• Protocols and communication layers
• High-level vs low-level protocols
• Internet communication basics
• Cloud computing basics
• IoT device concepts
Networking is commonly described as broad but introductory-level.
10. Software Development Life Cycle (SDLC)
• Planning and requirements gathering
• Analysis and design phases
• Coding and implementation
• Testing and debugging
• Deployment and maintenance
• Waterfall vs iterative thinking basics
• Computer problem-solving process phases
Students report SDLC terminology appears heavily in scenario questions.
11. Programming Paradigms
• Procedural programming
• Object-oriented programming
• Declarative programming
• Functional programming basics
• Language classification by paradigm
• Encapsulation and modularity basics
12. Ethics & Professional Responsibility
• ACM Code of Ethics
• IEEE Code of Ethics
• Responsible technology use
• Data privacy principles
• Intellectual property basics
• Ethical decision-making in computing
• Social impacts of technology
Ethics principles are commonly tested conceptually rather than memorization-only.
13. Commonly Reported “Most Tested” OA Areas
• Memory management techniques
• Searching and sorting algorithms
• Pseudocode interpretation
• Process states and PCB
• SDLC phases
• Abstract data types
• Operating system functions
• Networking basics
, Page 4 of 117
• IEEE and ACM ethics principles
• Absolute vs relative file paths
• Von Neumann architecture components
WGU D684 Introduction to Computer Science OA — MCQ Practice Batch 1 (1–50)
1. Which computational thinking concept involves breaking a large problem into smaller manageable
parts?
A. Abstraction
B. Pattern recognition
C. Decomposition
D. Generalization
Answer: C
Rationale: Decomposition divides complex problems into smaller sections that are easier to analyze and
solve systematically.
WGU D684 Introduction to Computer Science OA Exam
COMPLETE QUESTIONS AND DETAILED SOLUTIONS
LATEST UPDATE THIS YEAR JUST RELEASED
WGU D684 Introduction to Computer Science OA Exam — Detailed Coverage Guide
The WGU D684 Introduction to Computer Science OA (Objective Assessment) is a theory-heavy
foundational computer science exam that evaluates understanding of computer systems, algorithms,
programming logic, operating systems, networking, data structures, SDLC, and computational
thinking. Students consistently report that the OA closely aligns with the PA but is slightly more
scenario-based and terminology-focused.
1. Computational Thinking & Problem Solving
• Decomposition of complex problems
• Pattern recognition concepts
• Abstraction and generalization
• Algorithmic thinking and logical sequencing
• Step-by-step problem-solving process
• Computational models and automation basics
2. Computer Architecture Fundamentals
• Von Neumann architecture
• CPU components (ALU, Control Unit, Registers)
• Memory Unit functions
• Instruction Register vs Program Counter
• Fetch-decode-execute cycle
• Input/output devices and buses
• Multi-core processors and parallel processing
• Cache memory basics
3. Data Representation & Variables
• Binary number system basics
• Bits, bytes, and data encoding
• Variables and constants
• Primitive data types (int, float, Boolean, char, string)
• Strong typing concepts
• Type conversion and casting
• Assignment operations
4. Programming Logic & Pseudocode
• If/then and if/then/else logic
• Nested conditionals
• For loops and while loops
• Count-controlled vs event-controlled loops
• Functions and subprograms
• Parameters vs arguments
, Page 2 of 117
• Pseudocode tracing and interpretation
• Selecting correct logic for given scenarios
• Basic debugging concepts
Students report pseudocode interpretation is one of the most tested OA areas.
5. Algorithms & Complexity
• Definition and purpose of algorithms
• Linear search vs binary search
• Bubble sort, insertion sort, selection sort
• Basic algorithm efficiency concepts
• Sequential vs divide-and-conquer methods
• Complexity awareness (constant, linear, quadratic)
• Choosing the correct algorithm for scenarios
f(n)=n2f(n)=n^2f(n)=n2
6. Data Structures & Abstract Data Types
• Arrays and lists
• Linked lists
• Stacks (LIFO behavior)
• Queues (FIFO behavior)
• Abstract Data Types (ADT)
• Composite variables
• Data organization and retrieval methods
Students frequently mention stack vs queue questions on the OA.
7. Operating Systems
• Functions of an operating system
• Resource management responsibilities
• Process management fundamentals
• Process states: ready, running, waiting, terminated
• Process Control Block (PCB)
• Memory management techniques
• Fixed vs dynamic partitioning
• Virtual memory basics
• Paging and fragmentation
• File systems and OS interaction
Memory management is repeatedly identified as a major OA topic.
8. File Systems & Storage
• File organization principles
• Directories and folder structures
• Root directory concepts
• Absolute path vs relative path
• File allocation methods
• Storage devices and persistence
• Volatile vs non-volatile memory
9. Networking Fundamentals
, Page 3 of 117
• LAN, MAN, and WAN
• Ring, bus, and star topologies
• DNS and TLD concepts
• Routers, switches, and servers
• Protocols and communication layers
• High-level vs low-level protocols
• Internet communication basics
• Cloud computing basics
• IoT device concepts
Networking is commonly described as broad but introductory-level.
10. Software Development Life Cycle (SDLC)
• Planning and requirements gathering
• Analysis and design phases
• Coding and implementation
• Testing and debugging
• Deployment and maintenance
• Waterfall vs iterative thinking basics
• Computer problem-solving process phases
Students report SDLC terminology appears heavily in scenario questions.
11. Programming Paradigms
• Procedural programming
• Object-oriented programming
• Declarative programming
• Functional programming basics
• Language classification by paradigm
• Encapsulation and modularity basics
12. Ethics & Professional Responsibility
• ACM Code of Ethics
• IEEE Code of Ethics
• Responsible technology use
• Data privacy principles
• Intellectual property basics
• Ethical decision-making in computing
• Social impacts of technology
Ethics principles are commonly tested conceptually rather than memorization-only.
13. Commonly Reported “Most Tested” OA Areas
• Memory management techniques
• Searching and sorting algorithms
• Pseudocode interpretation
• Process states and PCB
• SDLC phases
• Abstract data types
• Operating system functions
• Networking basics
, Page 4 of 117
• IEEE and ACM ethics principles
• Absolute vs relative file paths
• Von Neumann architecture components
WGU D684 Introduction to Computer Science OA — MCQ Practice Batch 1 (1–50)
1. Which computational thinking concept involves breaking a large problem into smaller manageable
parts?
A. Abstraction
B. Pattern recognition
C. Decomposition
D. Generalization
Answer: C
Rationale: Decomposition divides complex problems into smaller sections that are easier to analyze and
solve systematically.
