Q#1: Probabilistic reasoning deals with:
(A) Reasoning under uncertainty
(B) Fully deterministic problems
(C) BFS nodes only
(D) DFS only
Answer: (A) Reasoning under uncertainty
Q#2: Bayes’ theorem is used to:
(A) Update probabilities based on evidence
(B) Compute deterministic outcomes
(C) BFS nodes only
(D) DFS only
Answer: (A) Update probabilities based on evidence
Q#3: Conditional probability P(A|B) represents:
(A) Probability of A given B is true
(B) Probability of A independent of B
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability of A given B is true
Q#4: Prior probability is:
(A) Belief before observing evidence
(B) Belief after evidence
(C) BFS nodes only
(D) DFS only
Answer: (A) Belief before observing evidence
Q#5: Posterior probability is:
(A) Updated belief after observing evidence
(B) Prior only
(C) BFS nodes only
(D) DFS only
Answer: (A) Updated belief after observing evidence
Q#6: Likelihood represents:
(A) Probability of evidence given hypothesis
(B) Probability of hypothesis
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability of evidence given hypothesis
Q#7: A Bayesian network represents:
(A) Probabilistic dependencies among variables
(B) Deterministic facts only
(C) BFS nodes only
(D) DFS only
Answer: (A) Probabilistic dependencies among variables
Q#8: Nodes in a Bayesian network represent:
(A) Random variables
(B) Actions only
(C) BFS nodes
(D) DFS only
Answer: (A) Random variables
Q#9: Directed edges in a Bayesian network represent:
(A) Conditional dependencies
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Conditional dependencies
Q#10: Conditional independence helps:
(A) Simplify probabilistic inference
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Simplify probabilistic inference
Q#11: D-separation is used to:
(A) Determine conditional independence in Bayesian networks
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Determine conditional independence in Bayesian networks
Q#12: Evidence in probabilistic reasoning refers to:
(A) Observed values of variables
(B) Unknown variables only
(C) BFS nodes
(D) DFS only
Answer: (A) Observed values of variables
Q#13: Exact inference methods include:
(A) Variable elimination, belief propagation
(B) Sampling only
(C) BFS nodes only
(D) DFS only
Answer: (A) Variable elimination, belief propagation
Q#14: Approximate inference methods include:
(A) Sampling methods like Monte Carlo
(B) Exact variable elimination
(C) BFS nodes only
(D) DFS only
Answer: (A) Sampling methods like Monte Carlo
Q#15: Marginalization is:
(A) Summing out variables to compute probabilities of interest
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Summing out variables to compute probabilities of interest
Q#16: Factor graphs are used for:
(A) Efficient computation of joint probabilities
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Efficient computation of joint probabilities
Q#17: Markov networks represent:
(A) Undirected probabilistic dependencies
(B) Directed dependencies only
(C) BFS nodes only
(D) DFS only
Answer: (A) Undirected probabilistic dependencies
Q#18: Noisy-OR model is used to:
(A) Model multiple causes of an effect efficiently
(B) Deterministic effects only
(C) BFS nodes only
(D) DFS only
Answer: (A) Model multiple causes of an effect efficiently
Q#19: Hidden Markov Models (HMMs) are used to:
(A) Model sequential probabilistic processes
(B) Single-step deterministic processes
(C) BFS nodes only
(D) DFS only
Answer: (A) Model sequential probabilistic processes
Q#20: HMM states are:
(A) Hidden variables
(B) Observed variables only
(C) BFS nodes only
(D) DFS only
Answer: (A) Hidden variables
Q#21: Observations in HMMs are:
(A) Evidence dependent on hidden states
(B) Independent of states
(C) BFS nodes only
(D) DFS only
Answer: (A) Evidence dependent on hidden states
Q#22: Filtering in HMMs computes:
(A) Current belief state given past observations
(B) Future state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Current belief state given past observations
Q#23: Prediction in HMMs computes:
(A) Future state probabilities
(B) Past state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Future state probabilities
Q#24: Smoothing in HMMs computes:
(A) Past state probabilities given all evidence
(B) Current state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Past state probabilities given all evidence
Q#25: Viterbi algorithm finds:
(A) Most probable sequence of hidden states
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Most probable sequence of hidden states
Q#26: Probabilistic reasoning supports:
(A) Diagnosis, prediction, decision-making
(B) Deterministic reasoning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Diagnosis, prediction, decision-making
Q#27: Decision networks include:
(A) Chance nodes, decision nodes, utility nodes
(B) Only chance nodes
(C) BFS nodes only
(D) DFS only
Answer: (A) Chance nodes, decision nodes, utility nodes
Q#28: Utility nodes represent:
(A) Preferences over outcomes
(B) Probabilities only
(C) BFS nodes only
(D) DFS only
Answer: (A) Preferences over outcomes
Q#29: Influence diagrams are used to:
(A) Make decisions under uncertainty
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Make decisions under uncertainty
Q#30: Expected utility combines:
(A) Probabilities and utilities of outcomes
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Probabilities and utilities of outcomes
Q#31: Belief update in probabilistic reasoning uses:
(A) Bayes’ rule
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Bayes’ rule
Q#32: Conditional probability tables (CPTs) store:
(A) Probability of a variable given its parents
(B) Random assignments only
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability of a variable given its parents
Q#33: Independence assumptions reduce:
(A) Complexity of inference
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Complexity of inference
Q#34: Approximate inference methods include:
(A) Sampling, particle filtering
(B) Variable elimination only
(C) BFS nodes only
(D) DFS only
Answer: (A) Sampling, particle filtering
Q#35: Particle filtering estimates:
(A) Belief states in dynamic systems
(B) Static deterministic states
(C) BFS nodes only
(D) DFS only
Answer: (A) Belief states in dynamic systems
Q#36: Real-world applications include:
(A) Robot localization, speech recognition, medical diagnosis
(B) Deterministic scheduling only
(C) BFS nodes only
(D) DFS only
Answer: (A) Robot localization, speech recognition, medical diagnosis
Q#37: Uncertain reasoning helps AI:
(A) Make rational decisions with incomplete information
(B) Only deterministic reasoning
(C) BFS nodes only
(D) DFS only
Answer: (A) Make rational decisions with incomplete information
Q#38: Evidence propagation in Bayesian networks is:
(A) Updating beliefs after observing evidence
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Updating beliefs after observing evidence
Q#39: Joint probability distribution defines:
(A) Probability over all variables
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Probability over all variables
Q#40: Marginal probability is computed by:
(A) Summing over other variables
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Summing over other variables
Q#41: Conditional probability tables reduce:
(A) Computation complexity
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Computation complexity
Q#42: Noisy-OR simplifies:
(A) Multiple causal effects in Bayesian networks
(B) Deterministic reasoning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Multiple causal effects in Bayesian networks
Q#43: Probabilistic reasoning is crucial for:
(A) Handling incomplete, noisy, or uncertain information
(B) Fully deterministic systems only
(C) BFS nodes only
(D) DFS only
Answer: (A) Handling incomplete, noisy, or uncertain information
Q#44: Decision-making under uncertainty uses:
(A) Probabilities and utilities
(B) Only probabilities
(C) BFS nodes only
(D) DFS only
Answer: (A) Probabilities and utilities
Q#45: Exact inference is feasible for:
(A) Small or sparse networks
(B) Large dense networks only
(C) BFS nodes only
(D) DFS only
Answer: (A) Small or sparse networks
Q#46: Approximate inference is needed for:
(A) Large or highly connected networks
(B) Small networks only
(C) BFS nodes only
(D) DFS only
Answer: (A) Large or highly connected networks
Q#47: Probabilistic reasoning allows:
(A) Prediction, diagnosis, decision-making, and learning
(B) Deterministic reasoning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Prediction, diagnosis, decision-making, and learning
Q#48: Belief propagation computes:
(A) Marginal probabilities efficiently
(B) Random assignments only
(C) BFS nodes only
(D) DFS only
Answer: (A) Marginal probabilities efficiently
Q#49: Probabilistic reasoning supports:
(A) Robust AI in uncertain and dynamic environments
(B) Only classical deterministic AI
(C) BFS nodes only
(D) DFS only
Answer: (A) Robust AI in uncertain and dynamic environments
Q#50: The main goal of probabilistic reasoning in AI is:
(A) Make rational inferences and decisions under uncertainty
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Make rational inferences and decisions under uncertainty