Q#1: A Constraint Satisfaction Problem consists of:
(A) Variables, Domains, Constraints
(B) States, Actions, Path Costs
(C) Sensors, Actuators, Feedback
(D) Heuristic functions only
Answer: (A) Variables, Domains, Constraints
Q#2: The domain of a variable is:
(A) Possible values it can take
(B) Constraints applied
(C) Path cost
(D) Neighboring nodes
Answer: (A) Possible values it can take
Q#3: A unary constraint involves:
(A) One variable
(B) Two variables
(C) Multiple variables
(D) No variables
Answer: (A) One variable
Q#4: A binary constraint involves:
(A) One variable
(B) Two variables
(C) Multiple variables
(D) Zero variables
Answer: (B) Two variables
Q#5: A global constraint involves:
(A) All variables in problem
(B) Single variable only
(C) Two variables
(D) Heuristic function
Answer: (A) All variables in problem
Q#6: A solution to CSP is:
(A) Assignment of values to all variables satisfying all constraints
(B) Random values
(C) Partial assignment only
(D) Heuristic estimate
Answer: (A) Assignment of values to all variables satisfying all constraints
Q#7: Backtracking search is:
(A) Depth-first search for CSP
(B) Breadth-first search
(C) Heuristic search
(D) Random search
Answer: (A) Depth-first search for CSP
Q#8: Forward checking helps to:
(A) Detect future conflicts
(B) Randomly assign values
(C) Ignore constraints
(D) BFS expansion
Answer: (A) Detect future conflicts
Q#9: Constraint propagation reduces:
(A) Domain size of variables
(B) Number of actions
(C) BFS nodes
(D) DFS depth
Answer: (A) Domain size of variables
Q#10: Arc-consistency ensures:
(A) Every value in a variable’s domain satisfies binary constraints with neighbors
(B) Path cost minimized
(C) BFS completed
(D) DFS depth limited
Answer: (A) Every value in a variable’s domain satisfies binary constraints with neighbors
Q#11: AC-3 algorithm is used for:
(A) Enforcing arc-consistency
(B) Random search
(C) DFS only
(D) BFS only
Answer: (A) Enforcing arc-consistency
Q#12: Degree heuristic chooses:
(A) Variable with most constraints on remaining variables
(B) Random variable
(C) Variable with least constraints
(D) Variable with largest domain
Answer: (A) Variable with most constraints on remaining variables
Q#13: Minimum Remaining Values (MRV) heuristic selects:
(A) Variable with smallest remaining domain
(B) Largest domain
(C) Random variable
(D) BFS node
Answer: (A) Variable with smallest remaining domain
Q#14: Least Constraining Value heuristic prefers:
(A) Value that rules out fewest options for neighbors
(B) Random value
(C) Largest value
(D) BFS node
Answer: (A) Value that rules out fewest options for neighbors
Q#15: CSP is complete if:
(A) All variables assigned and all constraints satisfied
(B) Partial assignment only
(C) BFS nodes expanded
(D) DFS depth limited
Answer: (A) All variables assigned and all constraints satisfied
Q#16: In CSP, failure occurs when:
(A) Variable has empty domain
(B) All variables assigned
(C) BFS expanded all nodes
(D) DFS depth reached
Answer: (A) Variable has empty domain
Q#17: Backtracking with heuristics improves:
(A) Search efficiency
(B) Memory usage
(C) BFS only
(D) DFS only
Answer: (A) Search efficiency
Q#18: Forward checking is applied:
(A) After each variable assignment
(B) At initial state only
(C) At goal only
(D) Randomly
Answer: (A) After each variable assignment
Q#19: Constraint propagation may involve:
(A) Arc-consistency
(B) Node-consistency
(C) Path-consistency
(D) All of the above
Answer: (D) All of the above
Q#20: Node-consistency ensures:
(A) Unary constraints satisfied for all domain values
(B) Binary constraints satisfied
(C) Path constraints satisfied
(D) BFS nodes valid
Answer: (A) Unary constraints satisfied for all domain values
Q#21: Path-consistency ensures:
(A) Binary constraints satisfied along paths of length 2
(B) Unary constraints satisfied
(C) DFS nodes valid
(D) BFS nodes valid
Answer: (A) Binary constraints satisfied along paths of length 2
Q#22: A CSP is over-constrained if:
(A) No solution exists
(B) Multiple solutions exist
(C) Single solution exists
(D) Random values work
Answer: (A) No solution exists
Q#23: A CSP is under-constrained if:
(A) Multiple solutions exist
(B) No solution exists
(C) Single solution exists
(D) Random assignment fails
Answer: (A) Multiple solutions exist
Q#24: Binary CSPs can be represented as:
(A) Constraint graph
(B) BFS tree
(C) DFS tree
(D) Heuristic table
Answer: (A) Constraint graph
Q#25: In constraint graph, nodes represent:
(A) Variables
(B) Values
(C) Constraints
(D) Actions
Answer: (A) Variables
Q#26: In constraint graph, edges represent:
(A) Constraints between variables
(B) Variable values
(C) Actions
(D) BFS nodes
Answer: (A) Constraints between variables
Q#27: Arc in arc-consistency represents:
(A) Directed binary constraint
(B) Value assignment
(C) BFS edge
(D) DFS depth
Answer: (A) Directed binary constraint
Q#28: Iterative improvement algorithms for CSP include:
(A) Min-Conflicts
(B) Genetic algorithms
(C) BFS
(D) DFS
Answer: (A) Min-Conflicts
Q#29: Min-Conflicts algorithm chooses:
(A) Variable that minimizes conflicts with current assignment
(B) Random variable
(C) Maximum conflicts variable
(D) BFS node
Answer: (A) Variable that minimizes conflicts with current assignment
Q#30: Min-Conflicts is suitable for:
(A) Large, randomly initialized CSPs
(B) Small problems only
(C) BFS trees
(D) DFS nodes
Answer: (A) Large, randomly initialized CSPs
Q#31: CSP search is complete if:
(A) Backtracking + forward checking + constraint propagation used
(B) BFS only
(C) DFS only
(D) Random search
Answer: (A) Backtracking + forward checking + constraint propagation used
Q#32: CSP complexity depends on:
(A) Number of variables
(B) Domain size
(C) Constraint density
(D) All of the above
Answer: (D) All of the above
Q#33: Map coloring is an example of:
(A) CSP
(B) Search problem
(C) Genetic algorithm
(D) Hill climbing
Answer: (A) CSP
Q#34: N-Queens problem is:
(A) CSP
(B) BFS problem
(C) DFS only
(D) Random search
Answer: (A) CSP
Q#35: Scheduling problems can be formulated as:
(A) CSP
(B) DFS only
(C) BFS only
(D) Hill climbing only
Answer: (A) CSP
Q#36: Sudoku is an example of:
(A) CSP
(B) Genetic algorithm
(C) Random search
(D) BFS only
Answer: (A) CSP
Q#37: Constraint satisfaction can be combined with:
(A) Backtracking
(B) Local search
(C) Heuristics
(D) All of the above
Answer: (D) All of the above
Q#38: In CSP, a dead-end occurs when:
(A) Variable domain is empty
(B) Solution found
(C) BFS completes
(D) DFS depth reached
Answer: (A) Variable domain is empty
Q#39: Most constrained variable heuristic helps to:
(A) Reduce backtracking
(B) Increase backtracking
(C) Random assignment
(D) BFS expansion
Answer: (A) Reduce backtracking
Q#40: Least constraining value heuristic improves:
(A) Forward checking efficiency
(B) DFS only
(C) BFS only
(D) Random search
Answer: (A) Forward checking efficiency
Q#41: CSPs with finite domains are:
(A) Discrete CSPs
(B) Continuous CSPs
(C) Random
(D) DFS only
Answer: (A) Discrete CSPs
Q#42: CSP with infinite domains requires:
(A) Continuous techniques
(B) BFS
(C) DFS only
(D) Random assignment
Answer: (A) Continuous techniques
Q#43: Constraint satisfaction is widely used in:
(A) Scheduling
(B) Planning
(C) Resource allocation
(D) All of the above
Answer: (D) All of the above
Q#44: Backjumping improves CSP search by:
(A) Skipping irrelevant variables on backtracking
(B) BFS expansion
(C) DFS depth limit
(D) Random moves
Answer: (A) Skipping irrelevant variables on backtracking
Q#45: Dynamic variable ordering improves:
(A) Backtracking efficiency
(B) BFS expansion
(C) DFS depth
(D) Random search
Answer: (A) Backtracking efficiency
Q#46: Constraint learning in CSP helps to:
(A) Avoid repeated conflicts
(B) Expand BFS
(C) DFS only
(D) Random assignments
Answer: (A) Avoid repeated conflicts
Q#47: CSP search can be solved using:
(A) Systematic search
(B) Local search
(C) Hybrid methods
(D) All of the above
Answer: (D) All of the above
Q#48: Forward checking + MRV is:
(A) Efficient for most CSPs
(B) Inefficient
(C) Random
(D) BFS only
Answer: (A) Efficient for most CSPs
Q#49: Constraint propagation improves:
(A) Efficiency by reducing domains
(B) BFS nodes
(C) DFS depth
(D) Random moves
Answer: (A) Efficiency by reducing domains
Q#50: Main goal of CSP solving is to:
(A) Find variable assignment satisfying all constraints
(B) Expand BFS tree
(C) DFS only
(D) Random assignment
Answer: (A) Find variable assignment satisfying all constraints