AI MCQ PDF - 2024-25 - USCS501

Smt. C.H.M College, Ulhasnagar-3 USCS501: Artificial Intelligence: Practice MCQS Foundations, History and State of the Art of AI...................................................................................... 1 Agents and Environments, Nature of Environ...

Smt. C.H.M College, Ulhasnagar-3 USCS501: Artificial Intelligence: Practice MCQS Foundations, History and State of the Art of AI...................................................................................... 1 Agents and Environments, Nature of Environments, Structure of Agents............................................. 4 Problem solving by searching............................................................................................................... 12 SVM....................................................................................................................................................... 19 Ensemble Learning................................................................................................................................ 21 Artificial Neural Networks..................................................................................................................... 23 Foundations, History and State of the Art of AI 1) What is Artificial intelligence? A. Putting your intelligence into Computer B. Programming with your own intelligence C. Making a Machine intelligent D. Playing a Game ANSWER: C 2) What is the goal of artificial intelligence? A. To solve real-world problems B. To solve artificial problems C. To explain various sorts of intelligence D. To extract scientific causes ANSWER: C 3) What of the following is considered to be a pivotal event in the history of AI? A. 1949, Donald O, The organization of Behavior. B. 1950, Computing Machinery and Intelligence. C. 1956, Dartmouth University Conference Organized by John McCarthy D. 1961, Computer and Computer Sense. ANSWER: C 4) Artificial Intelligence has its expansion in the following application. i) Planning and Scheduling ii) Game Playing iii) Robotics A. only i) B. only ii) C. only iii) Page 1 of 26  Smt. C.H.M College, Ulhasnagar-3 D. i), ii) and iii) ANSWER: D 5) Which among the following is/are the example of the intelligent agent/agents? i) Human ii) Robot iii) Autonomous Spacecraft A. only i) B. only ii) C. only iii) D. i), ii) and iii) ANSWER: D 6) The characteristics of the computer system capable of thinking, reasoning and learning is known as A. machine intelligence B. human intelligence C. artificial intelligence D. virtual intelligence ANSWER: C 7) Strong Artificial Intelligence is_______________ A. the embodiment of human intellectual capabilities within a computer B. a set of computer programs that produce output that would be considered to reflect intelligence if it were generated by humans C. the study of mental faculties through the use of mental models implemented on a computer D. Strong AI does not exists ANSWER: A 8) Traditional AI techniques still used today include all of the following EXCEPT: A. searching. B. heuristics. C. pattern recognition. D. parallel processing. ANSWER: D 9) A M. turing developed a technique for determining whether a computer could or could not demonstrate the artificial Intelligence, Presently, this technique is called A. Turing Test B. Algorithm C. Boolean Algebra D. Logarithm Page 2 of 26  Smt. C.H.M College, Ulhasnagar-3 ANSWER: A 10) The field that investigates the mechanics of human intelligence is: A. history B. cognitive science C. psychology D. sociology ANSWER: B 11) What is the name of the computer program that simulates the thought processes of human beings? A. Human logic B. Expert reason C. Expert system D. Personal information ANSWER: C 12) A completely automated chess game is based on A. Strong Artificial Intelligence approach B. Weak Artificial Intelligence approach C. Cognitive Artificial Intelligence approach D. Applied Artificial Intelligence approach ANSWER: A 13) The_____________adopted the idea that humans and animals can be considered information processing machines. A. Psychologists B. Neuroscientists C. Mathematicians D. Philosophers ANSWER: A 14) The________________ provided the tools to manipulate statements of logical certainty as well as uncertain, probabilistic statements. A. Psychologists B. Neuroscientists C. Mathematicians D. Philosophers ANSWER: C Page 3 of 26  Smt. C.H.M College, Ulhasnagar-3 15) _________deals with designing devices that acts optimally on the basis of feedback from the environment. A. Control theory B. Neuroscience C. Mathematics D. Philosophy ANSWER: A Agents and Environments, Nature of Environments, Structure of Agents. 16) An 'agent' is anything that: i) Perceives its environment through sensors and acting upon that environment through actuators ii) Takes input from the surroundings and uses its intelligence and performs the desired operations iii) A embedded program controlling line following robot A. i) and ii) B. ii) and iii) C. i) and iii) D. i), ii) and iii) ANSWER: D 17) Agents behavior can be best described by________________ A. Perception sequence B. Agent function C. Sensors and Actuators D. Environment in which agent is performing ANSWER: B 18) What is rational at any given time depends on: i) The performance measure that defines the criterion of success ii) The agent's prior knowledge of the environment iii) The actions that the agent can perform A. i) and ii) B. ii) and iii) C. i) and iii) D. i), ii) and iii) ANSWER: D Page 4 of 26  Smt. C.H.M College, Ulhasnagar-3 19) The Task Environment of an agent consists of: i) Sensors ii) Actuators iii) Performance Measures A. i) and ii) B. ii) and iii) C. i) and iii) D. i), ii) and iii) ANSWER: D 20) Crossword puzzle is ___________________. A. Fully Observable B. Partially Observable C. Not Observable D. Semi Observable ANSWER: A 21) An agent is composed of ________ A. Architecture B. Agent Function C. Perception Sequence D. Architecture and Program ANSWER: D 22) Robot machine might have cameras and infrared range finders for _________ and various motors for_________ A. Sensors, Agents B. Agents, Actuators C. Actuators, Sensors D. Sensors, Actuators ANSWER: D 23) What is meant by agent's percept sequence? A. Used to perceive the environment B. Complete history of actuator C. Complete history of perceived things D. Complete history of observed things ANSWER: C 24) What is the rule of simple reflex agent? A. Simple-action rule B. Condition-action rule C. simple-condition rule Page 5 of 26  Smt. C.H.M College, Ulhasnagar-3 D. action-reflex rule ANSWER: B 25) Which is used to improve the agent's performance? A. Perceiving B. Learning C. Observing D. Thinking ANSWER: B 26) Which element in agent is used for selecting external actions? A. Perceive B. Performance C. Learning D. Actuator ANSWER: D 27) Which depends on the percepts and actions available to the agent? A. Agent B. Sensor C. Design problem D. performance ANSWER: C 28) Which were built in such a way that humans had to supply the inputs and interpret the outputs? A. Agents B. AI system C. Sensor D. Actuators ANSWER: B 29) If the environment does not change while an agent is acting, then it is _______. A. Static B. Dynamic C. Episodic D. Deterministic ANSWER: A Page 6 of 26  Smt. C.H.M College, Ulhasnagar-3 30) If the agent's sensory apparatus can have access to the complete state of the environment, then the environment is __________ to that agent. A. Inaccessible B. Dynamic C. Accessible D. Deterministic ANSWER: C 31) If the next state of the environment is completely determined by the current state and the actions of the agent, then the environment is ____________ A. Accessible B. Dynamic C. Episodic D. Deterministic ANSWER: D 32) If it is possible to determine the complete state of the environment at each time point from the percepts, then the environment is ____________ A. Static B. Dynamic C. Observable D. Deterministic ANSWER: C 33) If there are a limited number of distinct, clearly defined, states of the environment, the environment is _____________ A. Discrete B. Continuous C. Episodic D. Deterministic ANSWER: A 34) For the agent as the self driving car, the environment should be____________ A. Discrete B. Continuous C. Episodic D. Deterministic ANSWER: B 35) For the agent as the CHESS game, the environment should be______________ A. Discrete B. Continuous Page 7 of 26  Smt. C.H.M College, Ulhasnagar-3 C. Episodic D. Deterministic ANSWER: A 36) What kind of observing environments are present in artificial intelligence? A. Partial B. Fully C. Learning D. Both Partial and Fully ANSWER: D 37) Which environment is called as semi dynamic? A. Environment does not change with the passage of time B. Agent performance changes C. Environment will be changed D. Environment does not change with the passage of time, but agent performance changes ANSWER: D 38) Where is the performance measure included? A. Rational agent B. Task environment C. Actuators D. Sensor ANSWER: B 39) What is state space? A. The whole problem B. Your Definition to a problem C. Problem you design D. Representing your problem with variable and parameter ANSWER: D 40) A search algorithm takes _________ as an input and returns ________ as an output. A. Input, output B. Problem, solution C. Solution, problem D. Parameters, sequence of actions ANSWER: B Page 8 of 26  Smt. C.H.M College, Ulhasnagar-3 41) An ideal rational agent is the one, which is capable of doing expected actions to maximize its performance measure, on the basis of______________ A. percept sequence B. built-in knowledge base C. problem D. percept sequence and built-in knowledge base ANSWER: D 42) The action of the Simple reflex agent completely depends upon A. Perception history B. Current perception C. Learning theory D. Utility functions ANSWER: B 43) A___________ is one that does the right thing conceptually speaking, every entry in the table for the agent function is filled out correctly. A. rational agent B. human agent C. software agent D. robotic agent ANSWER: A 44) A ________ replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors. A. rational agent B. human agent C. software agent D. robotic agent ANSWER: D 45) A ___________ has encoded bit strings as its programs and actions. A. rational agent B. human agent C. software agent D. robotic agent ANSWER: C 46) For the automated taxi driving problem, Safe, fast, legal, comfortable trip, maximize profits are________ A. Performance Measure B. Environment Page 9 of 26  Smt. C.H.M College, Ulhasnagar-3 C. Actuators D. Sensors ANSWER: A 47) For the automated taxi driving problem, Roads, other traffic, pedestrians are_________ A. Performance Measure B. Environment C. Actuators D. Sensors ANSWER: B 48) For the automated taxi driving problem, Steering, accelerator, brake are________ A. Performance Measure B. Environment C. Actuators D. Sensors ANSWER: C 49) For the automated taxi driving problem Cameras, sonar, speedometer, are_________ A. Performance Measure B. Environment C. Actuators D. Sensors ANSWER: D 50) For the agent as vacuum cleaner, the area to clean is_________ A. Performance Measure B. Environment C. Actuators D. Sensors ANSWER: D 51) We say an environment is ________if it is not fully observable or not deterministic. A. uncertain B. predictable C. stochastic D. deterministic ANSWER: A 52) An agent's ______________function is essentially an internalization of the performance measure. Page 10 of 26  Smt. C.H.M College, Ulhasnagar-3 A. goal B. predicate C. utility D. cost ANSWER: C 53) The task of ______________is to use observed rewards to learn an optimal (or nearly optimal) policy for the environment A. reinforcement learning B. machine learning C. adaptive dynamic programming D. neural network ANSWER: A 54) In ___________we are given a few labeled examples and must make what we can of a large collection of unlabeled examples. A. supervised learning B. unsupervised learning C. reinforcement learning D. semi-supervised learning ANSWER: D 55) Goal information along with current state description is neccessory in _________________. A. Simple Reflex Agent B. Model Based Agent C. Goal Based Agent D. Utility Based Agent ANSWER: C 56) ____________ is the start phase of an agent. A. Initial State B. Final State C. Goal State D. Path Cost ANSWER: A 57) A _________ means that the most recently generated node is chosen for expansion A. FIFO Queue B. LIFO Queue C. Priority Queue D. Stack Page 11 of 26  Smt. C.H.M College, Ulhasnagar-3 ANSWER: B Problem solving by searching 58) The objective of which of the following is to find a low-cost tour that starts from a city, visits all cities en-route exactly once and ends at the same starting city. A. Travelling Salesman Problem B. Hill-Climbing Search C. Greedy Best First Search D. Bidirectional Search ANSWER: A 59) Which of the following starts with an arbitrary solution to a problem and attempts to find a better solution by changing a single element of the solution incrementally. A. Travelling Salesman Problem B. Hill-Climbing Search C. Greedy Best First Search D. Bidirectional Search ANSWER: B 60) Which of the following searches forward from initial state and backward from goal state till both meet to identify a common state? A. Travelling Salesman Problem B. Hill-Climbing Search C. Greedy Best First Search D. Bidirectional Search ANSWER: D 61) Which search method takes less memory? A. Depth-First Search B. Breadth-First search C. Linear Search D. Optimal search ANSWER: A 62) Which search strategy is also called as blind search? A. Uninformed search B. Informed search C. Simple reflex search D. Linear search ANSWER: A Page 12 of 26  Smt. C.H.M College, Ulhasnagar-3 63) Which search is implemented with an empty first-in-first-out queue? A. Depth-first search B. Breadth-first search C. Bidirectional search D. None of the mentioned ANSWER: B 64) When is breadth-first search optimal? A. When there is less number of nodes B. When all step costs are equal C. When all step costs are unequal D. When there is more number of nodes ANSWER: B 65) Which search algorithm imposes a fixed depth limit on nodes? A. Depth-limited search B. Depth-first search C. Iterative deepening search D. Bidirectional search ANSWER: A 66) Which search implements stack operation for searching the states? A. Depth-limited search B. Depth-first search C. Breadth-first search D. None of the mentioned ANSWER: B 67) DFS is ______ efficient and BFS is __________ efficient. A. Space, Time B. Time, Space C. Time, Time D. Space, Space ANSWER: A 68) Which of the following algorithm is online search algorithm? A. Breadth-first search algorithm B. Depth-first search algorithm C. Hill-climbing search algorithm D. None of the mentioned ANSWER: C Page 13 of 26  Smt. C.H.M College, Ulhasnagar-3 69) What is the term used for describing the judgmental part of problem solving? A. Heuristic B. Critical C. Value based D. Analytical ANSWER: A 70) A heuristic is a way of trying: i) To discover something or an idea embedded in a program ii) To search and measure how far a node in a search tree seems to be from a goal iii) To compare two nodes in a search tree to see if one is better than the other A. i) and ii) B. ii) and iii) C. i) and iii) D. i), ii) and iii) ANSWER: D 71) What is a heuristic function? A. A function to solve mathematical problems B. A function which takes parameters of type string and returns an integer value C. A function whose return type is nothing D. A function that maps from problem state descriptions to measures of desirability. ANSWER: E 72) A* algorithm is based on A. Breadth-First-Search B. Depth-First –Search C. Best-First-Search D. Hill climbing. ANSWER: C 73) The actions in the vacuum world problem are______ A. left, right, suck B. up, down C. here and there D. stand still ANSWER: A 74) Initial state of the 8 – queens problem is________ A. No queens on the board B. All queens on board C. Any arrangement of 0 to 8 queens Page 14 of 26  Smt. C.H.M College, Ulhasnagar-3 D. More than 8 queens on the board ANSWER: A 75) Strategies that know whether one non-goal state is "more promising" than another are called _____________ A. uninformed search B. informed search C. blind search D. goal search ANSWER: B 76) Which of the following search strategies, expands the node n with the lowest path cost g(n). A. Breadth first search B. Depth first search C. Uniform-cost search D. A* search ANSWER: C 77) Which of the following search strategies, expands the deepest node in the current frontier of the search tree. A. Breadth first search B. Depth first search C. Uniform-cost search D. A* search ANSWER: B 78) Which of the following search strategies, evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost to get from the node to the goal: A. Breadth first search B. Bi – directional search C. Uniform-cost search D. A* search ANSWER: D 79) Which of the following search strategies, calls depth-first search with increasing depth limits until a goal is found A. Iterative deepening search B. Bi – directional search C. Uniform-cost search D. Depth first search ANSWER: A Page 15 of 26  Smt. C.H.M College, Ulhasnagar-3 80) Which of the following is optimal search algorithms that use limited amounts of memory A. Breadth first search B. Depth first search C. Recursive best first search D. A* search ANSWER: C 81) Best-First search is a type of informed search, which uses ________________ to choose the best next node for expansion. A. Evaluation function returning lowest evaluation B. Evaluation function returning highest evaluation C. Evaluation function returning lowest & highest evaluation D. None of them is applicable ANSWER: A 82) Heuristic function h(n) is ____ A. Lowest path cost B. Cheapest path from root to goal node C. Estimated cost of cheapest path from root to goal node D. Average path cost ANSWER: C 83) In greedy approach evaluation function is A. Heuristic function B. Path cost from start node to current node C. Path cost from start node to current node + Heuristic cost D. Average of Path cost from start node to current node and Heuristic cost ANSWER: A 84) The__________________ gives no information about the problem other than its definition. A. informed search algorithms B. uninformed search algorithms C. heuristics search algorithms D. Linear search algorithms ANSWER: B 85) The_____________is the process of deciding what actions and states to consider, given a goal. A. Problem formulation B. Goal formulation C. Predicate formulation Page 16 of 26  Smt. C.H.M College, Ulhasnagar-3 D. step formulation ANSWER: A 86) The state in the vacuum world problem is determined by _________and the____________. A. left and right B. Up and down C. agent location and dirt locations D. north and south ANSWER: C 87) A random variable with only one value(a coin that always comes up heads) has no uncertainty and thus its entropy is defined as _________ A. zero B. one C. equal D. can't determine ANSWER: A 88) Greedy search strategy chooses the node for expansion A. Shallowest B. Deepest C. The one closest to the goal node D. Minimum heuristic cost ANSWER: C 89) In greedy approach evaluation function is A. Heuristic function B. Path cost from start node to current node C. Path cost from start node to current node + Heuristic cost D. Average of Path cost from start node to current node and Heuristic cost ANSWER: A 90) How many types are available in uninformed search method? A. 3 B. 4 C. 5 D. 6 ANSWER: C 91) What is the space complexity of Depth-first search? Page 17 of 26  Smt. C.H.M College, Ulhasnagar-3 A. O(b) B. O(bl) C. O(m) D. O(bm) ANSWER: D 92) ____________________expands nodes with minimal f(n) = g(n) + h(n) A. BFS B. DFS C. IDA D. A* ANSWER: D 93) Which of the following performs search based on only heuristic functions? A. A* B. Greedy best first search C. Depth limited search D. Depth First Search ANSWER: A 94) The amount of memory needs to perform the search deals with _____________. A. Space Complexity B. Time Complexity C. Completeness D. Optimality ANSWER: A 95) The Uniform-Cost Search uses _____________ data structure. A. FIFO Queue B. LIFO Queue C. Priority Queue D. Stack ANSWER: C 96) The Depth-limited search solves the __________ problem. A. Finite-path B. Goal-path C. Infinite-path D. Successor-path ANSWER: C Page 18 of 26  Smt. C.H.M College, Ulhasnagar-3 SVM 97) By maximizing the distances between nearest data point and hyper plane will help us to decide the right hyper-plane. A. Margin B. Mercer's Theorem C. Regression D. Hyperplane ANSWER: A 98) What do you mean by generalization error in terms of the SVM? A. How far the hyperplane is from the support vectors B. How accurately the SVM can predict outcomes for unseen data C. The threshold amount of error in an SVM D. How simple the SVM is ANSWER: B 99) The effectiveness of an SVM depends upon: A. Selection of Kernel B. Kernel Parameters C. Soft Margin Parameter C D. Hard Margin ANSWER: D 100) The SVM's are less effective when: A. The data is linearly separable B. The data is clean and ready to use C. The data is noisy and contains overlapping points D. The data is non linear ANSWER: C 101) The cost parameter in the SVM means: A. The number of cross-validations to be made B. The kernel to be used C. The tradeoff between misclassification and simplicity of the model D. The number of datapoints ANSWER: C 102) Which of the following option would you more likely to consider iterating SVM next time? A. You want to increase your data points B. You want to decrease your data points C. You will try to calculate more variables Page 19 of 26  Smt. C.H.M College, Ulhasnagar-3 D. You will try to reduce the features ANSWER: C 103) Support vector machines (SVMs) are a set of _______ methods. A. supervised learning B. unsupervised learning C. semisupervised learning D. machine learning ANSWER: A 104) This 'lifting' of the data points represents the mapping of data into a higher dimension. This is known as ______ A. Kernelling B. Convex Optimization C. Hard Margin D. Soft Margin ANSWER: A 105) ______ cannot learn directly learned. A. system vector networks B. support vector networks C. Hyperparameters D. system vector navigation ANSWER: C 106) ____is used to learn a linear classifier to classify a non-linear dataset A. class variable B. dependent features C. kernel trick D. independent features ANSWER: C 107) SVMs attempt to minimize expected ______________ A. Generalization loss B. Empirical loss C. Errors D. Generalization loss and Empirical loss ANSWER: A 108) The maximum margin separator is at the________of the margin. A. Depth Page 20 of 26  Smt. C.H.M College, Ulhasnagar-3 B. Average C. zero distance from the margin D. midpoint ANSWER: D 109) __________ have the ability to embed the data into higher dimensional space. A. Nearest Neighbor Problem B. parametric model C. SVM D. Online learning ANSWER: C 110) A _______ is a line that separates the two classes. A. Decision boundary B. Regularization C. Classification D. SVM ANSWER: C Ensemble Learning 111) The idea of _____________ is to select a collection, or ensemble, of hypotheses from the hypothesis space and combine their predictions. A. Regression B. Ensemble learning C. k-nearest neighbor D. Support vector machine ANSWER: B 112) The most widely used ensemble method is called____________. A. boosting B. bagging C. stacking D. queuing ANSWER: A 113) In ensemble learning the higher the weight of an example, the ________ is the importance attached to it during the learning of a hypothesis. A. higher B. lower Page 21 of 26  Smt. C.H.M College, Ulhasnagar-3 C. can't predict D. medium ANSWER: A 114) The final___________ is a weighted-majority combination of all the K hypotheses, each weighted according to how well it performed on the training set. A. boosting hypothesis B. ensemble hypothesis C. parametric hypothesis D. non- parametric hypothesis ANSWER: B 115) Which of the following is an example of Ensemble learning_______ A. Neural Networks B. Adaboost C. Adaptive Dynamic Programming D. Bayes Theorem ANSWER: B 116) Learning method that is used to improve the classification, prediction, function approximation etc of a model A. Supervised Learning B. Unsupervised Learning C. Reinforcement Learning D. Ensemble learning ANSWER: D 117) A learning method that is used to solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined is called as A. Supervised Learning B. Unsupervised Learning C. Reinforcement Learning D. Ensemble learning ANSWER: D 118) The idea of ____________ is to select a collection of hypotheses from the hypothesis space and combine their predictions. A. Ensemble Learning B. SVM C. parametric model D. Online learning ANSWER: A Page 22 of 26  Smt. C.H.M College, Ulhasnagar-3 119) Which of the following is a kind of ensemble learning algorithm? A. supervised learning B. unsupervised learning C. parameterised learning D. adaboost ANSWER: D Artificial Neural Networks 120) Each neuron consists of cell body or soma which contains ______________. A. cell nucleus B. cell heart C. cell structure D. cell goal ANSWER: A 121) A neuron makes connections with 10 to 100,000 other neurons at junctions called _________________. A. axon B. dendrite C. synapses D. soma ANSWER: C 122) A perceptron is: A. a single layer feed-forward neural network with pre-processing B. an auto-associative neural network C. a double layer auto-associative neural network D. a neural network that contains feedback ANSWER: A 123) An auto-associative network is: A. a neural network that contains no loops B. a neural network that contains feedback C. a neural network that has only one loop D. a single layer feed-forward neural network with pre-processing ANSWER: B 124) A Neural Network can answer Page 23 of 26  Smt. C.H.M College, Ulhasnagar-3 A. For Loop questions B. what-if questions C. IF-The-Else Analysis Questions D. While Loop questions ANSWER: B 125) What is back propagation? A. It is another name given to the curvy function in the perceptron B. It is the transmission of error back through the network to adjust the inputs C. It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn D. It is the transmission of error back through the network to adjust the outputs ANSWER: C 126) Which of the following is not the promise of artificial neural network? A. It can explain result B. It can survive the failure of some nodes C. It has inherent parallelism D. It can handle noise ANSWER: A 127) Neural Networks are complex ______________ with many parameters. A. Linear Functions B. Nonlinear Functions C. Discrete Functions D. Exponential Functions ANSWER: A 128) An Artificial Neural Network is based on A. Strong Artificial Intelligence approach B. Weak Artificial Intelligence approach C. Cognitive Artificial Intelligence approach D. Applied Artificial Intelligence approach ANSWER: C 129) The network that involves backward links from output to the input and hidden layers is called as ________________ A. Self organizing maps B. Perceptrons C. Recurrent neural network D. Multi layered perceptron ANSWER: C Page 24 of 26  Smt. C.H.M College, Ulhasnagar-3 130) The fundamental unit of network is____________ A. brain B. nucleus C. neuron D. axon ANSWER: C 131) Feed forward networks are used for i) pattern mapping ii) pattern association iii) pattern classification A. i) and ii) B. ii) and iii) C. i and iii) D. i), ii) and iii) ANSWER: D 132) Ability to learn how to do tasks based on the data given for training or initial experience A. Self Organization B. Adaptive Learning C. Fault tolerance D. Robustness ANSWER: B 133) Each connection link in ANN is associated with ________ which has information about the input signal. A. neurons B. weights C. bias D. activation function ANSWER: B 134) Each neuron has a dummy weight with a value as ____ A. 1 B. 2 C. 3 D. 4 ANSWER: A Page 25 of 26  Smt. C.H.M College, Ulhasnagar-3 135) Which is not a component of a neuron? A. Input links B. Activation function C. Bias weight D. Learning rate ANSWER: D 136) A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. A. Decision tree B. Graphs C. Trees D. Neural Networks Answer: A 137) What is Decision Tree? A. Flow-Chart B. Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label C. Flow-Chart and Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label D. ER diagram Answer: C 138) Which of the following is not strength of Decision Tree ? A. able to generate understandable rules B. able to handle both continuous and categorical variables C. less appropriate for estimation tasks D. perform classification without requiring much computation ANSWER: C 139) Decision-tree algorithm falls under the category of ________ A. unsupervised learning algorithms B. reinforcement learning algorithm C. supervised learning algorithms D. prone to errors in classification problems with many class ANSWER: C 140) Suppose, your target variable is the price of a house using Decision Tree. What type of tree do you need to predict the target variable? A. classification tree B. regression tree C. clustering tree D. dimensionality reduction tree ANSWER: B Page 26 of 26