Artificial Intelligence Comprehensive Exam With
Correct Answers 2025/2026
1. Define Artificial Intelligence in the context of modern computing.
correct answer AI is the branch of computer science dedicated to creating systems capable
of performing tasks that typically require human intelligence. This includes processes such as
learning (the acquisition of information and rules for using it), reasoning (using rules to reach
approximate or definite conclusions), and self-correction. Modern AI often focuses on
"Narrow AI," designed for specific tasks like facial recognition or internet searches, as
opposed to "General AI," which would possess the broad cognitive abilities of a human.
2. What is the difference between Strong AI and Weak AI?
correct answer Weak AI, also known as Narrow AI, is designed and trained for a specific task;
it operates under a limited set of constraints and does not possess genuine consciousness
(e.g., virtual assistants like Siri). Strong AI, or Artificial General Intelligence (AGI), is a
theoretical form of AI where the machine possesses the ability to apply intelligence to any
problem, much like a human, exhibiting self-awareness and sentient behavior.
3. Explain the significance of the Turing Test.
correct answer Proposed by Alan Turing in 1950, the test assesses a machine's ability to
exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. If a
human evaluator cannot reliably tell the machine from the human based on text-based
conversation, the machine is said to have passed. It shifted the focus from "Can machines
think?" to "Can machines imitate human intelligence?"
4. Describe the concept of "Rational Agents" in AI.
correct answer A rational agent is an entity that perceives its environment through sensors
and acts upon that environment through actuators to achieve the best outcome or, when
there is uncertainty, the best expected outcome. Rationality is defined by the agent's
performance measure, its prior knowledge of the environment, the actions it can perform,
and its percept sequence.
5. What are "Heuristic Functions" in search algorithms?
correct answer A heuristic function, h(n), estimates the cost of the cheapest path from the
current node n to the goal node. Unlike exhaustive search methods, heuristics provide a way
to rank alternatives in search algorithms at each branching step based on available
information to decide which branch to follow, significantly reducing computation time in
complex state spaces.
6. Explain the Breadth-First Search (BFS) algorithm.
correct answer BFS is a graph traversal algorithm that starts at the tree root and explores all
neighboring nodes at the present depth level before moving on to the nodes at the next
depth level. It uses a queue data structure (FIFO) and is guaranteed to find the shallowest
goal state, making it complete and optimal if the path cost is a non-decreasing function of
the depth.
7. How does Depth-First Search (DFS) differ from BFS?
correct answer DFS explores as far as possible along each branch before backtracking. It uses
, a stack data structure (LIFO) and requires less memory than BFS because it only needs to
store the current path. However, it is not complete if the search space is infinite or contains
loops, and it is not guaranteed to find the most optimal path.
8. Describe the A* Search algorithm.
correct answer A* is an informed search algorithm that finds the shortest path between a
starting node and a goal node. It combines the strengths of Dijkstra’s algorithm (cost to reach
the node, g(n)) and Breadth-First Search (using a heuristic, h(n)). The algorithm minimizes
f(n) = g(n) + h(n), where f(n) is the total estimated cost of the lowest-cost solution through
node n.
9. Explain the Minimax Algorithm in game theory.
correct answer Minimax is a recursive backtracking algorithm used in decision-making and
game theory to find the optimal move for a player, assuming that the opponent is also
playing optimally. It minimizes the possible loss for a maximum loss (worst-case) scenario.
The "Max" player tries to maximize their score, while the "Min" player tries to minimize the
Max player's score.
10. What is Alpha-Beta Pruning?
correct answer Alpha-Beta Pruning is an optimization technique for the minimax algorithm.
It seeks to decrease the number of nodes evaluated by the minimax algorithm in its search
tree. It stops evaluating a move as soon as at least one possibility has been found that proves
the move to be worse than a previously examined move, thereby "pruning" branches that
cannot influence the final decision.
11. Define "Machine Learning" (ML).
correct answer Machine Learning is a subset of AI that provides systems the ability to
automatically learn and improve from experience without being explicitly programmed. It
focuses on the development of computer programs that can access data and use it to learn
for themselves by identifying patterns and making decisions based on statistical inference.
12. Explain Supervised Learning.
correct answer In supervised learning, the model is trained on a labeled dataset, meaning
each training example is paired with an output label. The algorithm learns a mapping
function from the input to the output. Once trained, the model can predict outcomes for
new, unseen data based on the patterns it identified during training.
13. Explain Unsupervised Learning.
correct answer Unsupervised learning involves training a model on data that does not have
explicit labels or outcomes. The goal is for the algorithm to find inherent structures, patterns,
or groupings within the data. Common tasks include clustering (grouping similar items) and
association (discovering rules that describe large portions of data).
14. What is Reinforcement Learning (RL)?
correct answer RL is a type of machine learning where an "agent" learns to make decisions
by performing actions in an environment to maximize a cumulative reward. It learns through
trial and error, receiving feedback in the form of rewards or penalties. It is widely used in
robotics, gaming, and navigation.
15. Describe the function of an Artificial Neural Network (ANN).
correct answer An ANN is a computational model inspired by the biological neural networks
Correct Answers 2025/2026
1. Define Artificial Intelligence in the context of modern computing.
correct answer AI is the branch of computer science dedicated to creating systems capable
of performing tasks that typically require human intelligence. This includes processes such as
learning (the acquisition of information and rules for using it), reasoning (using rules to reach
approximate or definite conclusions), and self-correction. Modern AI often focuses on
"Narrow AI," designed for specific tasks like facial recognition or internet searches, as
opposed to "General AI," which would possess the broad cognitive abilities of a human.
2. What is the difference between Strong AI and Weak AI?
correct answer Weak AI, also known as Narrow AI, is designed and trained for a specific task;
it operates under a limited set of constraints and does not possess genuine consciousness
(e.g., virtual assistants like Siri). Strong AI, or Artificial General Intelligence (AGI), is a
theoretical form of AI where the machine possesses the ability to apply intelligence to any
problem, much like a human, exhibiting self-awareness and sentient behavior.
3. Explain the significance of the Turing Test.
correct answer Proposed by Alan Turing in 1950, the test assesses a machine's ability to
exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. If a
human evaluator cannot reliably tell the machine from the human based on text-based
conversation, the machine is said to have passed. It shifted the focus from "Can machines
think?" to "Can machines imitate human intelligence?"
4. Describe the concept of "Rational Agents" in AI.
correct answer A rational agent is an entity that perceives its environment through sensors
and acts upon that environment through actuators to achieve the best outcome or, when
there is uncertainty, the best expected outcome. Rationality is defined by the agent's
performance measure, its prior knowledge of the environment, the actions it can perform,
and its percept sequence.
5. What are "Heuristic Functions" in search algorithms?
correct answer A heuristic function, h(n), estimates the cost of the cheapest path from the
current node n to the goal node. Unlike exhaustive search methods, heuristics provide a way
to rank alternatives in search algorithms at each branching step based on available
information to decide which branch to follow, significantly reducing computation time in
complex state spaces.
6. Explain the Breadth-First Search (BFS) algorithm.
correct answer BFS is a graph traversal algorithm that starts at the tree root and explores all
neighboring nodes at the present depth level before moving on to the nodes at the next
depth level. It uses a queue data structure (FIFO) and is guaranteed to find the shallowest
goal state, making it complete and optimal if the path cost is a non-decreasing function of
the depth.
7. How does Depth-First Search (DFS) differ from BFS?
correct answer DFS explores as far as possible along each branch before backtracking. It uses
, a stack data structure (LIFO) and requires less memory than BFS because it only needs to
store the current path. However, it is not complete if the search space is infinite or contains
loops, and it is not guaranteed to find the most optimal path.
8. Describe the A* Search algorithm.
correct answer A* is an informed search algorithm that finds the shortest path between a
starting node and a goal node. It combines the strengths of Dijkstra’s algorithm (cost to reach
the node, g(n)) and Breadth-First Search (using a heuristic, h(n)). The algorithm minimizes
f(n) = g(n) + h(n), where f(n) is the total estimated cost of the lowest-cost solution through
node n.
9. Explain the Minimax Algorithm in game theory.
correct answer Minimax is a recursive backtracking algorithm used in decision-making and
game theory to find the optimal move for a player, assuming that the opponent is also
playing optimally. It minimizes the possible loss for a maximum loss (worst-case) scenario.
The "Max" player tries to maximize their score, while the "Min" player tries to minimize the
Max player's score.
10. What is Alpha-Beta Pruning?
correct answer Alpha-Beta Pruning is an optimization technique for the minimax algorithm.
It seeks to decrease the number of nodes evaluated by the minimax algorithm in its search
tree. It stops evaluating a move as soon as at least one possibility has been found that proves
the move to be worse than a previously examined move, thereby "pruning" branches that
cannot influence the final decision.
11. Define "Machine Learning" (ML).
correct answer Machine Learning is a subset of AI that provides systems the ability to
automatically learn and improve from experience without being explicitly programmed. It
focuses on the development of computer programs that can access data and use it to learn
for themselves by identifying patterns and making decisions based on statistical inference.
12. Explain Supervised Learning.
correct answer In supervised learning, the model is trained on a labeled dataset, meaning
each training example is paired with an output label. The algorithm learns a mapping
function from the input to the output. Once trained, the model can predict outcomes for
new, unseen data based on the patterns it identified during training.
13. Explain Unsupervised Learning.
correct answer Unsupervised learning involves training a model on data that does not have
explicit labels or outcomes. The goal is for the algorithm to find inherent structures, patterns,
or groupings within the data. Common tasks include clustering (grouping similar items) and
association (discovering rules that describe large portions of data).
14. What is Reinforcement Learning (RL)?
correct answer RL is a type of machine learning where an "agent" learns to make decisions
by performing actions in an environment to maximize a cumulative reward. It learns through
trial and error, receiving feedback in the form of rewards or penalties. It is widely used in
robotics, gaming, and navigation.
15. Describe the function of an Artificial Neural Network (ANN).
correct answer An ANN is a computational model inspired by the biological neural networks
