Tutorials
PPTs
Lecture Notes
Objective Questions
Lab ProgramsObjective Type Questions & Answers
Artificial Intelligence (AI) MCQs - Unit-5
1. Acting under uncertainty involves:
A . Making decisions with incomplete or uncertain information
B . Solving deterministic problems
C . Using only propositional logic
D . Performing adversarial search
2. Basic probability notation includes:
A . Variables, events, and probabilities
B . Only variables and events
C . Only probabilities and quantifiers
D . Only quantifiers and connectives
3. Inference using full joint distributions involves:
A . Computing probabilities by summing over the joint distribution
B . Solving constraint satisfaction problems
C . Performing adversarial search
D . Using only propositional logic
4. Two events are independent if:
A . The occurrence of one does not affect the probability of the other
B . They always occur together
C . They are mutually exclusive
D . They are represented in a Bayesian network
5. Bayes’ rule is used to:
A . Update probabilities based on new evidence
B . Solve deterministic problems
C . Perform adversarial search
D . Use only propositional logic
6. The formula for Bayes’ rule is:
A . P(A∣B)=P(B∣A)⋅P(A)P(B)P(A∣B)=P(B)P(B∣A)⋅P(A)
B . P(A∣B)=P(A)+P(B)P(A∣B)=P(A)+P(B)
C . P(A∣B)=P(A)⋅P(B)P(A∣B)=P(A)⋅P(B)
D . P(A∣B)=P(A)−P(B)P(A∣B)=P(A)−P(B)
7. Which of the following is true about independence in probability?
A . Two events are independent if P(A∣B)=P(A)P(A∣B)=P(A)
B . Two events are independent if P(A∣B)=P(B)P(A∣B)=P(B)
C . Two events are independent if P(A∣B)=0P(A∣B)=0
D . Two events are independent if P(A∣B)=1P(A∣B)=1
8. Inference using full joint distributions is:
A . Computationally expensive for large domains
B . Only applicable to deterministic problems
C . Unrelated to probability
D . Only used in adversarial search
9. Bayes’ rule is particularly useful in:
A . Updating beliefs based on evidence
B . Solving constraint satisfaction problems
C . Performing adversarial search
D . Using only propositional logic
10. The probability of an event AA given event BB is denoted by:
A . P(A∣B)P(A∣B)
B . P(B∣A)P(B∣A)
C . P(A∩B)P(A∩B)
D . P(A∪B)P(A∪B)
11. Bayesian networks are used to:
A . Represent knowledge in uncertain domains
B . Solve deterministic problems
C . Perform adversarial search
D . Use only propositional logic
12. The semantics of Bayesian networks define:
A . The conditional independence relationships between variables
B . The syntax of the network
C . The inference rules
D . The quantifiers
13. Efficient representation of conditional distributions in Bayesian networks involves:
A . Using conditional probability tables
B . Solving constraint satisfaction problems
C . Performing adversarial search
D . Using only propositional logic
14. Approximate inference in Bayesian networks is used when:
A . Exact inference is computationally expensive
B . The network is small
C . The network is deterministic
D . Only propositional logic is used
15. Relational and first-order probability extend probabilistic reasoning to:
A . Handle relationships and objects
B . Solve deterministic problems
C . Perform adversarial search
D . Use only propositional logic
16. Dempster-Shafer theory is used for:
A . Reasoning with uncertainty and combining evidence
B . Solving deterministic problems
C . Performing adversarial search
D . Using only propositional logic
17. Which of the following is true about Bayesian networks?
A . They represent conditional dependencies between variables
B . They are only applicable to deterministic problems
C . They do not involve probability
D . They are unrelated to uncertain reasoning
18. Approximate inference methods in Bayesian networks include:
A . Sampling and variational methods
B . Only exact inference
C . Only constraint propagation
D . Only adversarial search
19. Relational probability models extend probabilistic reasoning to:
A . Handle relationships between objects
B . Solve deterministic problems
C . Perform adversarial search
D . Use only propositional logic
20. Dempster-Shafer theory is used to:
A . Combine evidence from multiple sources
B . Solve deterministic problems
C . Perform adversarial search
D . Use only propositional logic
Fill in the Blanks
21. Acting under uncertainty involves making decisions with __________ information.
22. Basic probability notation includes variables, events, and __________.
23. Inference using full joint distributions involves computing probabilities by __________ over the joint distribution.
24. Two events are independent if the occurrence of one does not affect the __________ of the other.
25. Bayes’ rule is used to update probabilities based on __________.
26. The formula for Bayes’ rule is P(A∣B)=P(A∣B)=.
27. Two events are independent if P(A∣B)=P(A∣B)=.
28. Inference using full joint distributions is computationally __________ for large domains.
29. Bayes’ rule is particularly useful in updating __________ based on evidence.
30. The probability of an event AA given event BB is denoted by __________.
31. Bayesian networks are used to represent knowledge in __________ domains.
32. The semantics of Bayesian networks define the __________ relationships between variables.
33. Efficient representation of conditional distributions in Bayesian networks involves using __________.
34. Approximate inference in Bayesian networks is used when exact inference is computationally __________.
35. Relational and first-order probability extend probabilistic reasoning to handle __________ and objects.
36. Dempster-Shafer theory is used for reasoning with __________ and combining evidence.
37. Bayesian networks represent __________ dependencies between variables.
38. Approximate inference methods in Bayesian networks include __________ and variational methods.
39. Relational probability models extend probabilistic reasoning to handle __________ between objects.
40. Dempster-Shafer theory is used to combine __________ from multiple sources.
Relevant Materials :
☞ Artificial Intelligence (AI) MCQs - Unit-1 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-2 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-3 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-4 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-5 - [ Artificial Intelligence ]
Similar Materials :
☞ R - Programming MCQs - Unit-1 - [ R-Programming ]
☞ R - Programming MCQs - Unit-2 - [ R-Programming ]
☞ R - Programming MCQs - Unit-3 - [ R-Programming ]
☞ R - Programming MCQs - Unit-4 - [ R-Programming ]
☞ R - Programming MCQs - Unit-5 - [ R-Programming ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-1 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-2 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-3 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-4 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-5 - [ FLAT ]
☞ PPS MCQs - Unit-1 - [ PPS ]
☞ PPS MCQs - Unit-2 - [ PPS ]
☞ PPS MCQs - Unit-3 - [ PPS ]
☞ PPS MCQs - Unit-4 - [ PPS ]
☞ PPS MCQs - Unit-5 - [ PPS ]
☞ Object Oriented Programming through Java MCQs - Unit-1 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-2 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-3 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-4 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-5 - [ OOP_JAVA ]
☞ Design and Analysis of Algorithms MCQs - Unit-1 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-2 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-3 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-4 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-5 - [ DAA ]
☞ Software Engineering MCQs - Unit-1 - [ SE ]
☞ Software Engineering MCQs - Unit-2 - [ SE ]
☞ Software Engineering MCQs - Unit-3 - [ SE ]
☞ Software Engineering MCQs - Unit-4 - [ SE ]
☞ Software Engineering MCQs - Unit-5 - [ SE ]
☞ Data Mining MCQs - Unit-1 - [ DM ]
☞ Data Mining MCQs - Unit-2 - [ DM ]
☞ Data Mining MCQs - Unit-3 - [ DM ]
☞ Data Mining MCQs - Unit-4 - [ DM ]
☞ Data Mining MCQs - Unit-5 - [ DM ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-1 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-2 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-3 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-4 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-5 - [ COA ]
☞ Data Structures Objective Type Question Bank-Unit-1 - [ DS ]
☞ Data Structures Objective Type Question Bank-Unit-2 - [ DS ]
☞ Data Structures Objective Type Question Bank-Unit-3 - [ DS ]
☞ Data Structures Objective Type Question Bank-Unit-4 - [ DS ]
☞ Data Structures Objective Type Question Bank-Unit-5 - [ DS ]
☞ Database Management System Objective Type Question Bank-Unit-1 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-2 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-3 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-4 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-5 - [ DBMS ]