CPE 126 INTRODUCTION TO ARTIFICIAL
INTELLIGENCE MODULE 3 EXAM | QUESTIONS AND
ANSWERS | 2026 UPDATE - MAPÚA UNIVERSITY.
149 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
CPE 126 INTRODUCTION TO ARTIFICIAL INTELLIGENCE MODULE 3 EXAM | QUESTIONS AND ANSWERS
| 2026 UPDATE - MAPÚA UNIVERSITY.. It contains 149 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 149 Questions
Foundations - Application - CPE 126 Introduction TO Artificial Intelligence Module 3 AND 2026 Update -
MAP A University Introduction TO Artificial Intelligence Module 3 Knowledge Representation Search AND
Machine Learning Foundations Undergraduate YEAR 3 BS Computer Engineering / BS Computer Science
MAP A University
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Introduction TO Artificial 1-25 Student, Search, Representation, Pruning, Network
Intelligence AND Intelligent
Agents
Problem Solving BY 26-50 Student, Agent, Network, Project, Search
Searching Uninformed AND
Informed Search
Adversarial Search AND 51-75 Student, Inference, Search, Directly, Network
GAME Playing
Knowledge Representation 76-100 Student, Search, Appropriate, Group, Campus
AND Reasoning
Propositional AND
First-order Logic
Uncertainty AND 101-125 Search, Heuristic, Campus, Algorithm, Agent
Probabilistic Reasoning
Bayesian Networks
Machine Learning 126-149 Student, Neural, Network, Robot, Representation
Fundamentals Supervised
Unsupervised Reinforcement
Learning
TOTAL 149 All questions include answers and detailed rationales
,Section A - Introduction TO Artificial Intelligence AND
Intelligent Agents
Q1.
A delivery robot must find the lowest-cost route in a grid where moving straight costs 1
and turning 90° costs 2. Which search strategy guarantees an optimal solution under
these conditions?
A. Breadth-First Search with a FIFO queue B. Depth-First Search with a LIFO stack
C. Uniform-Cost Search using a priority D. Greedy Best-First Search using
queue keyed on cumulative path cost Manhattan distance heuristic
Correct: C - Uniform-Cost Search using a priority queue keyed on cumulative path cost
Rationale:Uniform-Cost Search expands the node with the lowest cumulative path cost and
is optimal for nonnegative step costs, including the variable turn penalty. BFS is optimal only
when all step costs are equal, DFS is not optimal, and greedy best-first ignores path cost and
can return suboptimal routes.
Why the other answers are wrong:
A. BFS assumes uniform step cost and would not account for the extra turn penalty, so it may
return a higher-cost path.
B. DFS can wander deep into a costly branch and offers no optimality guarantee.
D. Greedy best-first uses only the heuristic estimate to the goal and can be misled into a path
with a larger true cost.
Reference: Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Ed., Ch. 3.4
(Uniform-Cost Search).
Q2.
Which statement correctly distinguishes propositional logic (PL) from first-order logic
(FOL) for knowledge representation?
A. FOL cannot express relations between B. PL supports quantifiers and variables,
objects, while PL can. while FOL does not.
C. FOL extends PL by adding objects, D. PL and FOL have identical expressive
relations, functions, and quantifiers, power but differ only in syntax.
enabling more compact domain encodings.
Correct: C - FOL extends PL by adding objects, relations, functions, and quantifiers,
enabling more compact domain encodings.
Page 3
, Section A - Introduction TO Artificial Intelligence AND Intelligent Agents
Rationale: FOL introduces terms, predicates, functions, and universal/existential quantifiers,
allowing generalization over objects; PL treats each proposition as an atomic symbol. This
makes FOL strictly more expressive and more compact for structured domains.
Why the other answers are wrong:
A. It reverses the relationship: PL lacks relations, while FOL supports them.
B. Quantifiers and variables are features of FOL, not PL.
D. Their expressive power differs; FOL is strictly more expressive than PL.
Reference: Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Ed., Ch. 8
(First-Order Logic).
Q3.
In a chess engine using minimax with alpha-beta pruning, which move ordering tends to
maximize pruning effectiveness?
A. Evaluate moves in random order at each B. Evaluate the worst-scoring move first at
node each node
C. Evaluate the best-scoring move first at D. Evaluate moves in the order they appear
each node in the move generator
Correct: C - Evaluate the best-scoring move first at each node
Rationale:Alpha-beta pruning prunes most effectively when the best move is examined first,
producing the tightest alpha/beta bounds early and reducing the effective branching factor to
about the square root of the original. Random or worst-first ordering yields little or no pruning
benefit.
Why the other answers are wrong:
A. Random ordering provides no systematic advantage and typically prunes far fewer nodes.
B. Worst-first ordering delays tightening the bounds and minimizes pruning.
D. Arbitrary ordering does not optimize pruning and may be close to random in effect.
Reference: Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Ed., Ch. 5.3
(Alpha-Beta Pruning).
Q4.
A Sudoku solver uses backtracking search with constraint propagation. Which technique
most directly reduces the branching factor during search?
A. Forward checking, which prunes values B. Iterative deepening, which repeatedly
inconsistent with assigned variables re-explores shallow nodes
C. Hill climbing, which moves to the best D. Simulated annealing, which accepts
neighbor state worse moves with probability
Correct: A - Forward checking, which prunes values inconsistent with assigned variables
Page 4
INTELLIGENCE MODULE 3 EXAM | QUESTIONS AND
ANSWERS | 2026 UPDATE - MAPÚA UNIVERSITY.
149 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
CPE 126 INTRODUCTION TO ARTIFICIAL INTELLIGENCE MODULE 3 EXAM | QUESTIONS AND ANSWERS
| 2026 UPDATE - MAPÚA UNIVERSITY.. It contains 149 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 149 Questions
Foundations - Application - CPE 126 Introduction TO Artificial Intelligence Module 3 AND 2026 Update -
MAP A University Introduction TO Artificial Intelligence Module 3 Knowledge Representation Search AND
Machine Learning Foundations Undergraduate YEAR 3 BS Computer Engineering / BS Computer Science
MAP A University
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Introduction TO Artificial 1-25 Student, Search, Representation, Pruning, Network
Intelligence AND Intelligent
Agents
Problem Solving BY 26-50 Student, Agent, Network, Project, Search
Searching Uninformed AND
Informed Search
Adversarial Search AND 51-75 Student, Inference, Search, Directly, Network
GAME Playing
Knowledge Representation 76-100 Student, Search, Appropriate, Group, Campus
AND Reasoning
Propositional AND
First-order Logic
Uncertainty AND 101-125 Search, Heuristic, Campus, Algorithm, Agent
Probabilistic Reasoning
Bayesian Networks
Machine Learning 126-149 Student, Neural, Network, Robot, Representation
Fundamentals Supervised
Unsupervised Reinforcement
Learning
TOTAL 149 All questions include answers and detailed rationales
,Section A - Introduction TO Artificial Intelligence AND
Intelligent Agents
Q1.
A delivery robot must find the lowest-cost route in a grid where moving straight costs 1
and turning 90° costs 2. Which search strategy guarantees an optimal solution under
these conditions?
A. Breadth-First Search with a FIFO queue B. Depth-First Search with a LIFO stack
C. Uniform-Cost Search using a priority D. Greedy Best-First Search using
queue keyed on cumulative path cost Manhattan distance heuristic
Correct: C - Uniform-Cost Search using a priority queue keyed on cumulative path cost
Rationale:Uniform-Cost Search expands the node with the lowest cumulative path cost and
is optimal for nonnegative step costs, including the variable turn penalty. BFS is optimal only
when all step costs are equal, DFS is not optimal, and greedy best-first ignores path cost and
can return suboptimal routes.
Why the other answers are wrong:
A. BFS assumes uniform step cost and would not account for the extra turn penalty, so it may
return a higher-cost path.
B. DFS can wander deep into a costly branch and offers no optimality guarantee.
D. Greedy best-first uses only the heuristic estimate to the goal and can be misled into a path
with a larger true cost.
Reference: Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Ed., Ch. 3.4
(Uniform-Cost Search).
Q2.
Which statement correctly distinguishes propositional logic (PL) from first-order logic
(FOL) for knowledge representation?
A. FOL cannot express relations between B. PL supports quantifiers and variables,
objects, while PL can. while FOL does not.
C. FOL extends PL by adding objects, D. PL and FOL have identical expressive
relations, functions, and quantifiers, power but differ only in syntax.
enabling more compact domain encodings.
Correct: C - FOL extends PL by adding objects, relations, functions, and quantifiers,
enabling more compact domain encodings.
Page 3
, Section A - Introduction TO Artificial Intelligence AND Intelligent Agents
Rationale: FOL introduces terms, predicates, functions, and universal/existential quantifiers,
allowing generalization over objects; PL treats each proposition as an atomic symbol. This
makes FOL strictly more expressive and more compact for structured domains.
Why the other answers are wrong:
A. It reverses the relationship: PL lacks relations, while FOL supports them.
B. Quantifiers and variables are features of FOL, not PL.
D. Their expressive power differs; FOL is strictly more expressive than PL.
Reference: Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Ed., Ch. 8
(First-Order Logic).
Q3.
In a chess engine using minimax with alpha-beta pruning, which move ordering tends to
maximize pruning effectiveness?
A. Evaluate moves in random order at each B. Evaluate the worst-scoring move first at
node each node
C. Evaluate the best-scoring move first at D. Evaluate moves in the order they appear
each node in the move generator
Correct: C - Evaluate the best-scoring move first at each node
Rationale:Alpha-beta pruning prunes most effectively when the best move is examined first,
producing the tightest alpha/beta bounds early and reducing the effective branching factor to
about the square root of the original. Random or worst-first ordering yields little or no pruning
benefit.
Why the other answers are wrong:
A. Random ordering provides no systematic advantage and typically prunes far fewer nodes.
B. Worst-first ordering delays tightening the bounds and minimizes pruning.
D. Arbitrary ordering does not optimize pruning and may be close to random in effect.
Reference: Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Ed., Ch. 5.3
(Alpha-Beta Pruning).
Q4.
A Sudoku solver uses backtracking search with constraint propagation. Which technique
most directly reduces the branching factor during search?
A. Forward checking, which prunes values B. Iterative deepening, which repeatedly
inconsistent with assigned variables re-explores shallow nodes
C. Hill climbing, which moves to the best D. Simulated annealing, which accepts
neighbor state worse moves with probability
Correct: A - Forward checking, which prunes values inconsistent with assigned variables
Page 4
