ARTIFICIAL INTELLIGENCE
50-Item Multiple Choice Examination
Modules 1–4 Coverage
Name: __RISSAN O. GUILLERGAN ____________________
Date: ___MAY 16, 2026______________________________
Score: _____________________________________________
INSTRUCTIONS: Choose the best answer for each item. Write the letter of your answer on the blank
before the number.
MODULE 1 — Introduction to Artificial Intelligence (Items 1–13)
_B__ 1. What is Artificial Intelligence?
A) A branch of mathematics focused on statistics
B) A field of computer science that creates systems capable of performing tasks requiring
human intelligence
C) A type of hardware designed for fast computation
D) A programming language used for data analysis
_C_ 2. Which of the following is NOT listed as a characteristic of intelligent systems?
A) Learning ability
B) Logical reasoning
C) Random output generation
D) Self-correction
_C__ 3. In traditional programming, how are outputs produced?
A) Through probabilistic models
B) By learning from data
C) Through explicit, fixed instructions
D) Via automatic rule discovery
_C__ 4. Which AI branch focuses on enabling computers to understand human language?
A) Computer Vision
B) Robotics
C) Natural Language Processing
D) Expert Systems
__C_ 5. A recommendation engine (e.g., Netflix) is an example of which AI application?
A) Robotics
B) Expert Systems
C) Real-world AI applications
D) Computer Vision
_A__ 6. Which of the following is an ethical concern related to AI?
A) Algorithm bias
B) Faster internet speeds
C) Improved battery life
D) Better screen resolution
_B__ 7. How does AI programming differ from traditional programming in terms of rules?
A) AI uses manual rules set by programmers
B) AI discovers rules automatically from data
C) AI requires no rules at all
D) AI uses only Boolean logic rules
_D__ 8. Which AI branch is primarily concerned with physical machines that can perform tasks?
A) Machine Learning
B) Expert Systems
C) Natural Language Processing
D) Robotics
_B__ 9. Which of the following best describes 'adaptability' as a characteristic of intelligent systems?
A) The ability to follow fixed rules precisely
B) The ability to adjust behavior in response to new environments or data
C) The ability to generate random outputs
D) The ability to store large amounts of data
_C__ 10. Medical AI systems are an example of AI application in which domain?
A) Navigation
B) Fraud detection
C) Healthcare
D) Entertainment
_B__ 11. What is one risk associated with job automation driven by AI?
A) Faster internet connectivity
B) Displacement of human workers
C) Improved algorithm bias
D) Reduced privacy risks
_B__ 12. Which AI branch enables machines to interpret and understand images?
A) Machine Learning
B) Computer Vision
C) Robotics
D) Natural Language Processing
_C__ 13. Which characteristic of AI systems refers to its ability to identify and fix its own mistakes?
A) Decision making
B) Adaptability
C) Self-correction
D) Logical reasoning
MODULE 2 — Problem Solving & Search Algorithms (Items 14–25)
_B__ 14. What are the five formal components used in AI problem formulation?
A) Variables, Domains, Constraints, Goal, Cost
B) Initial State, Actions, Transition Model, Goal Test, Path Cost
C) Start, End, Path, Heuristic, Algorithm
D) Nodes, Edges, Weights, Heuristic, Solution
_C__ 15. Which data structure does Depth-First Search (DFS) use?
A) Queue (FIFO)
B) Priority Queue
C) Stack (LIFO)
D) Hash Table
_B__ 16. Which search algorithm is guaranteed to find the shortest path?
A) Depth-First Search
B) Breadth-First Search
C) Greedy Search
D) Hill Climbing
_D__ 17. What data structure does Breadth-First Search (BFS) use?
A) Stack
B) Tree
C) Graph
D) Queue
_A__ 18. What is the Manhattan Distance formula for a grid problem?
A) sqrt((x1-x2)² + (y1-y2)²)
B) |x1 - x2| + |y1 - y2|
C) (x1 × x2) + (y1 × y2)
D) max(|x1-x2|, |y1-y2|)
_C__ 19. In the A* algorithm, what does f(n) represent?
A) The heuristic estimate from node n to the goal
B) The cost from the start to node n
C) The estimated total cost through node n to the goal
D) The number of nodes explored
_B__ 20. What is the difference between uninformed and informed search?
A) Uninformed search uses heuristics; informed search does not
B) Informed search uses heuristics to guide the search; uninformed search does not
C) Uninformed search is always faster
D) Informed search always guarantees the optimal path
_C__ 21. Why is Manhattan distance considered an 'admissible' heuristic?
A) It always overestimates the true cost
B) It is easy to compute
C) It never overestimates the true cost to the goal
D) It uses Euclidean geometry
_C__ 22. In A* search, g(n) represents:
A) The heuristic estimate from n to the goal
B) The total estimated cost
C) The actual cost from the start node to node n
D) The number of successors of node n
_B__ 23. In the 8-Puzzle problem, what is the initial state?
A) The goal configuration of the tiles
B) The current arrangement of tiles in the 3×3 grid
C) The set of all possible moves
D) The path cost of each move
_C__ 24. Which Python module is recommended for implementing an efficient queue in BFS?
A) heapq
B) queue
C) collections.deque
D) stack
__C_ 25. In which scenario would DFS be preferred over BFS?
A) When the shortest path must be guaranteed
B) When the search space is wide but shallow
C) When memory is limited and the goal is deep in the search tree
D) When multiple shortest paths exist
MODULE 3 — Knowledge Representation & CSP (Items 26–38)
_B__ 26. What are the three pillars introduced in Module 3?
A) Search, Planning, and Learning
B) Knowledge Representation, Logic, and Constraint Satisfaction Problems
C) Facts, Rules, and Ontologies
D) Variables, Domains, and Algorithms
_C_ 27. In propositional logic, what does the connective '∧ ' represent?
A) OR
B) NOT
C) AND
D) IMPLIES
_B__ 28. What is a semantic network in knowledge representation?
A) A collection of IF-THEN production rules
B) A web of nodes and labeled relationships representing concepts
C) A table of truth values
D) A Python dictionary of facts
_B__ 29. What does the universal quantifier '∀ ' mean in First-Order Logic?
A) There exists
B) For all
C) Not equal to
D) Implies
_B__ 30. What are the three components of a Constraint Satisfaction Problem (CSP)?
A) Nodes, Edges, and Weights
B) Variables, Domains, and Constraints
C) States, Actions, and Goals
D) Facts, Rules, and Inference
_C__ 31. In the Map Coloring CSP, what do the domains represent?
A) The states or territories to be colored
B) The border constraints between states
C) The possible colors that can be assigned to each region
D) The number of adjacent neighbors
_C__ 32. What heuristic in CSP selects the variable with the fewest remaining legal values?
A) Least Constraining Value (LCV)
B) Degree Heuristic
C) Minimum Remaining Values (MRV)
D) Arc Consistency (AC-3)
_B__ 33. What is the relationship between backtracking search in CSPs and DFS?
A) They are completely unrelated algorithms
B) Backtracking is DFS with constraint checking at each assignment step
C) Backtracking uses BFS with heuristics
D) Backtracking replaces DFS in all AI problems
_C__ 34. What does arc consistency (AC-3) do in CSP solving?
A) Assigns values to variables in alphabetical order
B) Checks if a solution is complete
C) Prunes domain values that cannot satisfy constraints as a preprocessing step
D) Finds the optimal path using heuristics
_C_ 35. What is a tautology in propositional logic?
A) A formula that is always false
B) A formula that is sometimes true
C) A formula that is always true regardless of variable values
D) A formula that can be satisfied by at least one assignment
_B__ 36. In First-Order Logic, what is unification?
A) The process of converting English to FOL
B) Finding a substitution that makes two expressions identical
C) Applying all rules to derive new facts
D) Merging two knowledge bases
_D__ 37. Which knowledge representation type uses IF-THEN rules?
A) Semantic Networks
B) Ontologies
C) Frames / Schemas
D) Production Rules
_B_ 38. What does the existential quantifier '∃ ' mean in First-Order Logic?
A) For all
B) There exists at least one
C) Not equal
D) Therefore
MODULE 4 — Machine Learning Fundamentals (Items 39–50)
_B__ 39. What is the key difference between Machine Learning and traditional AI (search/logic)?
A) ML uses explicit rules programmed by engineers
B) ML algorithms discover patterns from data instead of following hand-coded rules
C) ML requires more memory than traditional AI
D) ML is only used for image recognition tasks
_C__ 40. A spam filter trained on emails labeled 'spam' or 'not spam' is an example of:
A) Unsupervised Learning
B) Reinforcement Learning
C) Supervised Learning
D) Deep Learning
_D_ 41. An agent that learns to play chess by winning and losing games is an example of:
A) Supervised Learning
B) Unsupervised Learning
C) Transfer Learning
D) Reinforcement Learning
_B__ 42. What is k-Nearest Neighbours (k-NN) called a 'lazy learner'?
A) It uses an inefficient algorithm
B) It requires no training phase — it stores training data and classifies at prediction time
C) It produces inaccurate results
D) It only works with small datasets
_C__ 43. What distance metric does the basic k-NN algorithm use?
A) Manhattan Distance
B) Cosine Similarity
C) Euclidean Distance
D) Hamming Distance
_C__ 44. What does a confusion matrix show in model evaluation?
A) The training time of the model
B) The number of features used
C) The counts of correct and incorrect predictions broken down by class
D) The decision boundaries of the classifier
_B__ 45. What does 'overfitting' mean in machine learning?
A) The model is too simple to learn from the training data
B) The model memorizes training data and performs poorly on unseen data
C) The model uses too few features
D) The training dataset is too large
_C_ 46. What is the purpose of keeping a separate test set during model training?
A) To speed up the training process
B) To increase the size of the training data
C) To provide an unbiased evaluation of model performance on unseen data
D) To reduce the number of features
_C__ 47. The F1-Score is calculated as:
A) Correct / Total
B) TP / (TP + FP)
C) 2 × (Precision × Recall) / (Precision + Recall)
D) TP / (TP + FN)
_B__ 48. What does cross-validation help to estimate?
A) The optimal number of features to use
B) A more reliable measure of real-world model performance by averaging over multiple
train/test splits
C) The exact number of training epochs needed
D) The best learning rate for a neural network
_B__ 49. The Iris dataset used in Module 4 contains how many samples and classes?
A) 100 samples, 2 classes
B) 150 samples, 3 classes
C) 200 samples, 4 classes
D) 120 samples, 3 classes
_C__ 50. How do Decision Trees relate to CSP backtracking from Module 3?
A) Both use BFS to find solutions
B) They are unrelated algorithms from different fields
C) Decision trees make decisions at each node based on feature thresholds, similar to CSP
backtracking which makes assignments based on constraints
D) Decision trees use constraint propagation to prune branches