AI and ML Quiz practice set 04th October 2026 Share On Que: (1). What does Artificial Intelligence primarily aim to develop? Systems used only for numerical calculations B. Systems that can only store large amounts of data C. Systems capable of performing tasks requiring human-like intelligence A. More than one of the above D. None of the above E. Check Answer Open question page Que: (2). Which of the following best defines Artificial Intelligence? The ability of a computer to store unlimited data B. The ability of a machine to perform tasks that normally require human intelligence A. The process of converting analog signals into digital signals C. More than one of the above D. None of the above E. Check Answer Open question page Que: (3). Which of the following is a fundamental goal of Artificial Intelligence? To enable machines to learn, reason and make decisions intelligently A. To eliminate the need for computer hardware B. To make all computer programs deterministic C. More than one of the above D. None of the above E. Check Answer Open question page Que: (4). Which characteristic of an AI system enables it to improve its performance using experience or data? Formatting C. Compilation B. Learning A. More than one of the above D. None of the above E. Check Answer Open question page Que: (5). Which of the following is a common application of Artificial Intelligence? Speech recognition A. Medical diagnosis support B. Recommendation systems C. More than one of the above D. None of the above E. Check Answer Open question page Que: (6). Artificial Intelligence is primarily considered a subfield of which discipline? Civil Engineering B. Computer Science A. Mechanical Engineering C. More than one of the above D. None of the above E. Check Answer Open question page Que: (7). Which of the following is NOT generally considered a major subfield or area of Artificial Intelligence? Computer Vision B. Natural Language Processing A. Robotics C. More than one of the above D. None of the above E. Check Answer Open question page Que: (8). Which statement best describes Machine Learning as a part of Artificial Intelligence? It enables systems to learn patterns from data and improve performance A. It requires every possible rule to be manually programmed B. It is concerned only with computer hardware design C. More than one of the above D. None of the above E. Check Answer Open question page Que: (9). What is the primary purpose of Natural Language Processing (NLP)? To design computer processors B. To enable computers to understand and process human language A. To manage physical network cables C. More than one of the above D. None of the above E. Check Answer Open question page Que: (10). What is the primary objective of AI-based robotics? To replace all computer networks B. To increase only the storage capacity of computers C. To enable robots to perceive their environment and perform intelligent actions A. More than one of the above D. None of the above E. Check Answer Open question page Que: (11). What is the main objective of Computer Vision in Artificial Intelligence? To enable computers to interpret and understand visual information A. To manage database transactions C. To increase the clock speed of processors B. More than one of the above D. None of the above E. Check Answer Open question page Que: (12). What is an Expert System in Artificial Intelligence? A system used only for storing multimedia files B. A computer system without any predefined knowledge C. A system that uses a knowledge base and inference mechanism to solve problems in a specific domain A. More than one of the above D. None of the above E. Check Answer Open question page Que: (13). Which statement correctly describes the relationship between Artificial Intelligence and Machine Learning? Machine Learning is a subset or approach within Artificial Intelligence A. Both terms always have exactly the same meaning C. Artificial Intelligence is a subset of Machine Learning B. More than one of the above D. None of the above E. Check Answer Open question page Que: (14). Which statement correctly distinguishes Artificial Intelligence from Deep Learning? Artificial Intelligence is a subset of Deep Learning B. Deep Learning is a subset of Machine Learning, which is itself a major approach within AI A. Deep Learning and AI are completely unrelated fields C. More than one of the above D. None of the above E. Check Answer Open question page Que: (15). Which of the following represents a major advantage and limitation of Artificial Intelligence respectively? Fast data processing and decision support; dependence on data and computational resources A. Complete elimination of human supervision; zero implementation cost C. No requirement for data; unlimited accuracy B. More than one of the above D. None of the above E. Check Answer Open question page Que: (16). What is an intelligent agent in Artificial Intelligence? A computer program that can only perform arithmetic operations B. An entity that perceives its environment and acts upon it to achieve objectives A. A device used exclusively for data storage C. More than one of the above D. None of the above E. Check Answer Open question page Que: (17). Which statement best describes the relationship between an agent and its environment? The environment only stores the agent program B. The agent and environment always have identical states C. The agent perceives the environment and performs actions that can affect it A. More than one of the above D. None of the above E. Check Answer Open question page Que: (18). In an intelligent agent, what is meant by perception and action? Perception means changing the hardware and action means storing data B. Both perception and action refer only to internal memory operations C. Perception is receiving information from the environment, while action is the agent response to that information A. More than one of the above D. None of the above E. Check Answer Open question page Que: (19). What is a rational agent in Artificial Intelligence? An agent that never interacts with its environment C. An agent that always selects a random action B. An agent that chooses actions expected to maximize its performance measure based on available information A. More than one of the above D. None of the above E. Check Answer Open question page Que: (20). Which of the following is a standard classification of intelligent agents? Analog, digital and hybrid processors C. Compiler, interpreter and assembler agents B. Simple reflex, model-based reflex, goal-based, utility-based and learning agents A. More than one of the above D. None of the above E. Check Answer Open question page Que: (21). What is the main characteristic of a simple reflex agent? It requires a utility function for every decision C. It always maintains a complete history of the environment B. It selects actions based on the current percept using condition-action rules A. More than one of the above D. None of the above E. Check Answer Open question page Que: (22). What distinguishes a model-based reflex agent from a simple reflex agent? A model-based reflex agent maintains an internal state representing aspects of the environment A. A model-based reflex agent never uses previous percept information C. A model-based reflex agent cannot perceive its environment B. More than one of the above D. None of the above E. Check Answer Open question page Que: (23). What is the defining feature of a goal-based agent? It can only respond using fixed condition-action rules B. It selects actions by considering whether they help achieve a specified goal A. It does not require any information about the environment C. More than one of the above D. None of the above E. Check Answer Open question page Que: (24). What is the primary role of a utility function in a utility-based agent? To measure the desirability or usefulness of possible outcomes A. To convert machine code into source code B. To identify only the physical sensors of an agent C. More than one of the above D. None of the above E. Check Answer Open question page Que: (25). What is the main distinguishing feature of a learning agent? It can improve its performance through experience A. It cannot interact with its environment C. It can only execute permanently fixed rules B. More than one of the above D. None of the above E. Check Answer Open question page Que: (26). Which of the following is a basic characteristic of an intelligent agent? Complete dependence on continuous human instructions B. Autonomy and the ability to perceive and act in an environment A. Inability to respond to environmental changes C. More than one of the above D. None of the above E. Check Answer Open question page Que: (27). What does PEAS represent in the specification of an intelligent agent? Program, Environment, Algorithm and Software B. Performance measure, Environment, Actuators and Sensors A. Performance, Execution, Algorithm and Storage C. More than one of the above D. None of the above E. Check Answer Open question page Que: (28). What is the purpose of a performance measure in an intelligent-agent system? To evaluate how successfully an agent is achieving its objectives A. To determine the programming language used by the agent C. To specify only the physical dimensions of the agent B. More than one of the above D. None of the above E. Check Answer Open question page Que: (29). In the context of intelligent agents, what is an environment? The source code of the agent program C. Only the internal memory of an agent B. The external world in which the agent operates and with which it interacts A. More than one of the above D. None of the above E. Check Answer Open question page Que: (30). What is the function of actuators in an intelligent agent? They store the complete history of all percepts C. They enable the agent to perform actions on the environment A. They are used only to collect information from the environment B. More than one of the above D. None of the above E. Check Answer Open question page Que: (31). What is the primary function of sensors in an intelligent agent? To perceive information from the environment A. To execute the agent's program instructions C. To physically modify the environment B. More than one of the above D. None of the above E. Check Answer Open question page Que: (32). What is problem formulation in Artificial Intelligence? The process of converting source code into machine code B. The process of defining the initial state, goal state, actions and other elements required to solve a problem A. The process of storing all possible solutions in memory C. More than one of the above D. None of the above E. Check Answer Open question page Que: (33). What is meant by state-space representation of a problem? Representation of computer memory locations only C. Representation of only the final solution of a problem B. Representation of a problem using possible states and transitions between those states A. More than one of the above D. None of the above E. Check Answer Open question page Que: (34). What does the initial state represent in a state-space problem? The starting condition from which the search begins A. The set of all possible operators C. The condition that always represents the final solution B. More than one of the above D. None of the above E. Check Answer Open question page Que: (35). What is a goal state in an AI search problem? A state that can never be reached from the initial state C. The first state generated by a search algorithm B. A state that satisfies the conditions specified by the problem objective A. More than one of the above D. None of the above E. Check Answer Open question page Que: (36). What are operators or actions in a state-space search problem? The memory locations used by the search algorithm C. Rules or actions that transform one state into another A. The final states that terminate every search B. More than one of the above D. None of the above E. Check Answer Open question page Que: (37). What is a search tree in Artificial Intelligence? A tree containing only the final goal states B. A data structure used exclusively for sorting numbers C. A tree structure representing the states generated during the search process A. More than one of the above D. None of the above E. Check Answer Open question page Que: (38). What is meant by the search space of a problem? The set of all possible states that can be considered while solving the problem A. Only the initial state of a problem B. Only the states that are already part of the final solution C. More than one of the above D. None of the above E. Check Answer Open question page Que: (39). What is an uninformed search strategy? A search strategy that does not use problem-specific heuristic information A. A search strategy that always uses a heuristic function B. A search strategy that knows the exact path to the goal in advance C. More than one of the above D. None of the above E. Check Answer Open question page Que: (40). Which data structure is primarily used by Breadth First Search (BFS) to explore nodes level by level? Queue A. Priority queue based only on heuristic value C. Stack B. More than one of the above D. None of the above E. Check Answer Open question page Que: (41). Which data structure is primarily associated with Depth First Search (DFS)? Queue B. Hash table only C. Stack A. More than one of the above D. None of the above E. Check Answer Open question page Que: (42). What is the main characteristic of Depth-Limited Search? It performs depth-first search up to a specified depth limit A. It uses only heuristic values to select nodes C. It always searches the entire state space B. More than one of the above D. None of the above E. Check Answer Open question page Que: (43). What is the basic idea behind Iterative Deepening Search? It uses only heuristic information without considering depth C. It performs DFS only once with an unlimited depth B. It repeatedly performs depth-limited search with increasing depth limits A. More than one of the above D. None of the above E. Check Answer Open question page Que: (44). What is an informed search strategy? A search strategy that never uses any information about the goal B. A search strategy that uses additional problem-specific knowledge to guide the search A. A search strategy that examines states in completely random order C. More than one of the above D. None of the above E. Check Answer Open question page Que: (45). What is heuristic search in Artificial Intelligence? A search technique that never evaluates generated states B. A search technique that always explores states in alphabetical order C. A search technique that uses an estimate of the cost or distance to the goal to guide exploration A. More than one of the above D. None of the above E. Check Answer Open question page Que: (46). What does a heuristic function h(n) generally represent in an AI search problem? The total number of nodes in the search tree C. The exact cost already spent from the initial state to node n B. An estimated cost from node n to a goal state A. More than one of the above D. None of the above E. Check Answer Open question page Que: (47). What criterion is commonly used by Greedy Best First Search to select the next node? The path cost g(n) only B. The depth of the node only C. The heuristic value h(n) A. More than one of the above D. None of the above E. Check Answer Open question page Que: (48). Which evaluation function is used by the A* search algorithm? f(n) = g(n) + h(n) A. f(n) = g(n) × h(n) C. f(n) = g(n) - h(n) B. More than one of the above D. None of the above E. Check Answer Open question page Que: (49). What is the main idea of the Hill Climbing search technique? It always guarantees the globally optimal solution C. It always explores every node at the same depth before proceeding B. It repeatedly moves to a neighbouring state that appears better according to an evaluation function A. More than one of the above D. None of the above E. Check Answer Open question page Que: (50). What is knowledge representation in Artificial Intelligence? A technique used only for increasing processor speed C. A method used only to compress computer files B. A method of representing knowledge about the real world in a form that an AI system can use for reasoning A. More than one of the above D. None of the above E. Check Answer Open question page Que: (51). What is a knowledge base in an Artificial Intelligence system? A database containing only multimedia files B. A collection of computer hardware components C. A collection of facts and rules representing knowledge about a particular domain A. More than one of the above D. None of the above E. Check Answer Open question page Que: (52). In knowledge representation, what is a fact? A condition that must always be false B. A statement that represents information known to be true A. A procedure used to execute a computer program C. More than one of the above D. None of the above E. Check Answer Open question page Que: (53). What is the role of a rule in a knowledge-based AI system? A rule represents the physical hardware of an AI system C. A rule is used only to store images B. A rule specifies a logical relationship between conditions and conclusions A. More than one of the above D. None of the above E. Check Answer Open question page Que: (54). What is inference in Artificial Intelligence? The process of deriving new conclusions from known facts and rules A. The process of deleting all information from a knowledge base B. The process of converting source code into machine code C. More than one of the above D. None of the above E. Check Answer Open question page Que: (55). Which statement best describes propositional logic? A programming language used to create operating systems C. A formal logic in which statements are represented as propositions that can be true or false A. A logic used only for numerical calculations B. More than one of the above D. None of the above E. Check Answer Open question page Que: (56). What is the main advantage of Predicate or First-Order Logic over basic propositional logic? It cannot use variables or predicates C. It can represent only statements with no internal structure B. It can represent objects, properties and relationships between objects A. More than one of the above D. None of the above E. Check Answer Open question page Que: (57). What is a semantic network in knowledge representation? A graph-based representation in which nodes represent concepts and links represent relationships A. A database containing only numerical values C. A network used only for transmitting computer packets B. More than one of the above D. None of the above E. Check Answer Open question page Que: (58). What is a frame in Artificial Intelligence knowledge representation? A hardware component used to execute AI algorithms B. A structured representation used to describe an object or concept using attributes and associated values A. A network protocol used for communication C. More than one of the above D. None of the above E. Check Answer Open question page Que: (59). What is forward chaining in a rule-based AI system? A method that randomly selects rules without using facts C. A data-driven inference method that starts with known facts and applies rules to derive new facts A. A method that starts only with a goal and works backward B. More than one of the above D. None of the above E. Check Answer Open question page Que: (60). What is backward chaining in a rule-based AI system? A method used only for sorting data C. A goal-driven inference method that starts with a goal and works backward to find supporting facts A. A method that always starts with all available facts and derives every possible conclusion B. More than one of the above D. None of the above E. Check Answer Open question page Que: (61). What is an expert system in Artificial Intelligence? A system used only for storing large amounts of numerical data B. A computer system that performs only basic arithmetic operations C. An AI system that uses stored domain knowledge and reasoning to solve problems like a human expert A. More than one of the above D. None of the above E. Check Answer Open question page Que: (62). Which of the following is a characteristic of an expert system? It can work only without any stored knowledge C. It uses domain-specific knowledge to provide expert-level advice or decisions A. It must always replace human experts completely B. More than one of the above D. None of the above E. Check Answer Open question page Que: (63). What is the primary role of the knowledge base in an expert system? To store domain-specific facts, rules and knowledge A. To display the graphical interface only C. To provide the physical hardware required by the system B. More than one of the above D. None of the above E. Check Answer Open question page Que: (64). What is the function of the inference engine in an expert system? It applies rules to known facts to derive conclusions A. It is responsible only for collecting sensor data C. It stores only the user interface design B. More than one of the above D. None of the above E. Check Answer Open question page Que: (65). What is the purpose of the user interface in an expert system? To provide communication between the user and the expert system A. To replace the inference engine C. To store all domain knowledge permanently B. More than one of the above D. None of the above E. Check Answer Open question page Que: (66). What is knowledge acquisition in an expert system? The process of deleting the inference engine B. The process of designing only the graphical user interface C. The process of obtaining and incorporating knowledge from experts and other sources into the knowledge base A. More than one of the above D. None of the above E. Check Answer Open question page Que: (67). What is a rule-based expert system? An expert system that contains no knowledge base B. An expert system that can operate only as a database C. An expert system that represents knowledge mainly using IF-THEN rules A. More than one of the above D. None of the above E. Check Answer Open question page Que: (68). Which of the following is a common application of expert systems? Medical diagnosis and decision support A. Fault diagnosis in technical systems B. Financial and business decision support C. More than one of the above D. None of the above E. Check Answer Open question page Que: (69). Which of the following correctly describes an advantage and a limitation of expert systems? They require no domain knowledge, while their main limitation is excessive human intelligence C. Consistent decision support is an advantage, while knowledge acquisition and maintenance can be difficult A. Unlimited general intelligence is an advantage, while low accuracy is always a limitation B. More than one of the above D. None of the above E. Check Answer Open question page Que: (70). Which of the following best defines Machine Learning? A technique in which computers learn patterns from data and improve their performance without being explicitly programmed for every task A. A method used only for designing computer hardware B. A programming language used to create artificial intelligence programs C. A technique used only for storing large amounts of data D. None of the above E. Check Answer Open question page Que: (71). Which characteristic of Machine Learning enables a system to improve its performance using experience or data? Manual hardware configuration C. Fixed rule execution B. Learning from data A. Static data storage D. None of the above E. Check Answer Open question page Que: (72). Which statement correctly distinguishes Artificial Intelligence from Machine Learning? AI and ML are completely unrelated fields C. Machine Learning is a subset of Artificial Intelligence A. Artificial Intelligence is a subset of Machine Learning B. Machine Learning is limited to robotics only D. None of the above E. Check Answer Open question page Que: (73). In Machine Learning, what does the term dataset refer to? A single instruction given to a computer B. A collection of data used for analysis, learning, evaluation, or testing of a machine learning system A. Only the final output produced by a model C. A hardware component used for machine learning D. None of the above E. Check Answer Open question page Que: (74). In a Machine Learning dataset, what is a feature? The final decision made by the model B. The complete training algorithm C. An input variable or measurable characteristic used by a model to learn patterns A. The hardware on which the model runs D. None of the above E. Check Answer Open question page Que: (75). In supervised Machine Learning, what is a label? The known target or desired output associated with an input example A. A variable used only for identifying the computer B. The algorithm used to train a model C. The number of features in a dataset D. None of the above E. Check Answer Open question page Que: (76). What is the primary purpose of training data in Machine Learning? To enable the model to learn patterns or relationships from examples A. To replace the learning algorithm C. To permanently store the final predictions only B. To measure only the hardware performance of a computer D. None of the above E. Check Answer Open question page Que: (77). What is the primary purpose of testing data in Machine Learning? To create the programming language used by the model C. To directly modify the model parameters during training B. To evaluate how well a trained model performs on previously unseen data A. To replace all training data D. None of the above E. Check Answer Open question page Que: (78). In Machine Learning, what is a model? A raw dataset that has not been processed B. A learned representation or function that maps input data to an output or prediction A. A physical storage device C. A programming editor used to write ML code D. None of the above E. Check Answer Open question page Que: (79). What is meant by prediction in Machine Learning? The process of collecting raw data only B. The output or estimated result produced by a trained model for given input data A. The process of writing source code manually C. The removal of all features from a dataset D. None of the above E. Check Answer Open question page Que: (80). Which statement correctly describes the training and testing process in Machine Learning? Training data is used to learn the model, while testing data is used to evaluate its performance on unseen examples A. Training and testing data must always contain exactly the same records C. Testing data is always used before training data B. Testing data is used only to increase the size of the training dataset D. None of the above E. Check Answer Open question page Que: (81). What is meant by the learning process in Machine Learning? The process of converting software into hardware C. The process of adjusting or estimating model parameters from data to improve performance on a task A. The process of manually writing every decision rule B. The process of deleting the training dataset D. None of the above E. Check Answer Open question page Que: (82). What does generalization mean in Machine Learning? The ability of a trained model to perform well on new, previously unseen data A. The ability to memorize every training example exactly B. The process of increasing the size of the computer memory C. The process of removing all test data D. None of the above E. Check Answer Open question page Que: (83). Which type of Machine Learning uses labeled training data to learn a mapping between inputs and outputs? Reinforcement Learning C. Supervised Learning A. Unsupervised Learning B. Random Learning D. None of the above E. Check Answer Open question page Que: (84). Which type of Machine Learning attempts to discover hidden patterns or structures in data without predefined labels? Supervised Learning A. Reinforcement Learning C. Unsupervised Learning B. Rule-based Learning D. None of the above E. Check Answer Open question page Que: (85). Which type of Machine Learning learns through interaction with an environment using rewards or penalties? Reinforcement Learning C. Unsupervised Learning B. Supervised Learning A. Semi-supervised Learning D. None of the above E. Check Answer Open question page Que: (86). Which statement best describes Semi-supervised Learning? It uses only unlabeled data C. It uses a combination of a small amount of labeled data and a larger amount of unlabeled data A. It uses only labeled data B. It does not use any training data D. None of the above E. Check Answer Open question page Que: (87). What is classification in supervised Machine Learning? A process of predicting only continuous numerical values C. A process of assigning input data to one or more predefined classes A. A process of grouping data without any predefined structure B. A process of selecting computer hardware D. None of the above E. Check Answer Open question page Que: (88). Which of the following best describes the basic concept of classification? Learning a decision boundary or relationship that separates data into predefined categories A. Finding clusters without using any labeled data C. Predicting only continuous numerical quantities B. Maximizing computer memory utilization D. None of the above E. Check Answer Open question page Que: (89). What is the basic principle of the K-Nearest Neighbors (KNN) algorithm? A decision tree is always constructed before classification B. A new data point is classified according to the classes of its nearest training examples A. The algorithm assumes that all features are independent C. The algorithm uses only the oldest training example D. None of the above E. Check Answer Open question page Que: (90). Which statement correctly describes a Decision Tree in Machine Learning? It can only solve unsupervised learning problems C. It represents decisions using a tree-like structure of tests, branches, and outcomes A. It always requires all features to be statistically independent B. It is used only for storing training datasets D. None of the above E. Check Answer Open question page Que: (91). What is the fundamental assumption used by the Naive Bayes classifier? Features are assumed to be conditionally independent given the class A. All features must have identical values B. The dataset must contain no categorical features C. The classes must always have equal probability D. None of the above E. Check Answer Open question page Que: (92). What is the basic idea behind a Support Vector Machine (SVM)? To find a decision boundary that separates classes while maximizing the margin between them A. To always construct a decision tree C. To group data randomly into different classes B. To predict only time-series values D. None of the above E. Check Answer Open question page Que: (93). What is the basic purpose of Logistic Regression in Machine Learning? To find the shortest path in a graph C. To construct only hierarchical clusters B. To estimate the probability of an observation belonging to a class A. To store labeled data in a database D. None of the above E. Check Answer Open question page Que: (94). What is regression in supervised Machine Learning? A supervised learning technique used to predict continuous numerical values A. A method used exclusively for finding clusters C. A technique used only to classify images into categories B. A method that does not require training data D. None of the above E. Check Answer Open question page Que: (95). Which statement best describes the concept of regression? It always produces only categorical output B. It works without any training examples C. It learns a relationship between input variables and a continuous output variable A. It is used only for dimensionality reduction D. None of the above E. Check Answer Open question page Que: (96). What is Linear Regression? A regression technique that models the relationship between variables using a linear function A. A clustering technique based only on distances C. A classification technique that always creates decision trees B. A technique used only for categorical outputs D. None of the above E. Check Answer Open question page Que: (97). What is Simple Linear Regression? A linear regression model involving one independent variable and one dependent variable A. A regression model that must contain at least ten independent variables B. A classification algorithm based on nearest neighbors C. A model that cannot make numerical predictions D. None of the above E. Check Answer Open question page Que: (98). What is the basic idea of Multiple Linear Regression? It predicts a continuous dependent variable using two or more independent variables A. It is an unsupervised clustering algorithm C. It predicts a class using only one categorical feature B. It does not use any independent variables D. None of the above E. Check Answer Open question page Que: (99). Which statement correctly distinguishes classification from regression? Classification predicts only continuous values, whereas regression predicts only categories B. Classification predicts categorical classes, whereas regression generally predicts continuous numerical values A. Both classification and regression always produce identical types of output C. Regression does not require training data D. None of the above E. Check Answer Open question page Que: (100). Which statement correctly distinguishes training data from testing data? Testing data is always used to train the model B. Training and testing data must always contain exactly the same records C. Training data is used to learn the model, while testing data is used to evaluate the trained model on unseen examples A. Testing data is used only for increasing the number of features D. None of the above E. Check Answer Open question page Que: (101). Which of the following is a major advantage of supervised learning? It never requires labeled data B. It can learn a mapping from inputs to known target outputs and make predictions for new data A. It cannot be used for prediction C. It always produces perfectly accurate results D. None of the above E. Check Answer Open question page Que: (102). What is the primary objective of clustering in Unsupervised Learning? To group similar data objects together without using predefined class labels A. To classify data using predefined labels only C. To predict a known target value from labeled data B. To calculate only the accuracy of a supervised model D. None of the above E. Check Answer Open question page Que: (103). Which statement correctly describes K-Means Clustering? It always creates a hierarchical tree of clusters C. It requires every training example to have a predefined class label B. It divides data into a specified number of clusters by assigning observations to the nearest cluster centroid A. It is primarily used to predict continuous target values D. None of the above E. Check Answer Open question page Que: (104). What is the basic idea of Hierarchical Clustering? It always requires the number of clusters to be fixed before learning B. It uses labeled data to train a classification model C. It creates a hierarchy of clusters that can be represented using a tree-like structure called a dendrogram A. It can only be used for regression problems D. None of the above E. Check Answer Open question page Que: (105). What is Association Rule Learning primarily used for? Discovering relationships or co-occurrence patterns among items in a dataset A. Predicting only continuous numerical values B. Assigning every observation to a predefined class C. Reducing the number of features using eigenvectors D. None of the above E. Check Answer Open question page Que: (106). What is the basic purpose of Principal Component Analysis (PCA)? To assign predefined class labels to data B. To reduce the dimensionality of data while retaining as much important variation as possible A. To divide data into exactly K clusters C. To generate association rules from transaction data D. None of the above E. Check Answer Open question page Que: (107). What is the basic concept of Reinforcement Learning? Grouping data without using any feedback C. Learning only from labeled input-output pairs B. Learning through interaction with an environment using rewards or penalties as feedback A. Learning only by storing previously observed data D. None of the above E. Check Answer Open question page Que: (108). In Reinforcement Learning, what is the role of an agent? The agent only stores the training dataset B. The agent observes the environment, selects actions, and learns from the resulting rewards A. The agent provides predefined class labels for supervised learning C. The agent only measures the size of the environment D. None of the above E. Check Answer Open question page Que: (109). What is meant by a policy in Reinforcement Learning? A dataset containing only labeled examples B. A strategy that determines which action an agent should take in a given state A. A measure of the physical size of the environment C. A fixed reward value for every possible action D. None of the above E. Check Answer Open question page Que: (110). What is the exploration-exploitation trade-off in Reinforcement Learning? The agent must balance trying new actions with choosing actions that are already known to provide good rewards A. The agent must always repeat the first action it learns C. The agent must always choose a completely random action B. The agent must avoid receiving rewards during learning D. None of the above E. Check Answer Open question page Que: (111). What is the basic idea of Q-Learning? It learns the expected value of taking an action in a particular state and uses these values to select better actions A. It requires all training examples to have predefined class labels B. It groups data into clusters using only Euclidean distance C. It predicts continuous values using a straight-line equation D. None of the above E. Check Answer Open question page Que: (112). What is the basic function of an artificial neuron in a neural network? It only performs database operations C. It stores the complete training dataset permanently B. It processes input values using weights and an activation function to produce an output A. It randomly generates the output without using input values D. None of the above E. Check Answer Open question page Que: (113). Which statement best describes a Perceptron? An unsupervised clustering algorithm B. A basic single-layer neural model that can be used for binary classification A. A database indexing technique C. A reinforcement learning environment D. None of the above E. Check Answer Open question page Que: (114). Which statement correctly describes the layers of a neural network? The input layer receives data, hidden layers perform intermediate processing, and the output layer produces the final result A. The output layer always contains the raw input data C. The hidden layer only stores the original dataset B. All layers perform exactly the same function D. None of the above E. Check Answer Open question page Que: (115). What is the purpose of an activation function in a neural network? To introduce non-linearity and determine the output of a neuron based on its input A. To divide a dataset into training and testing sets C. To permanently store training data B. To replace the weights of all neurons with zero D. None of the above E. Check Answer Open question page Que: (116). Which statement correctly describes Deep Learning and its major neural network applications? Deep Learning is limited to simple linear regression B. CNNs and RNNs are database management algorithms C. Deep Learning uses neural networks with multiple layers, while CNNs are commonly used for image-related tasks and RNNs are designed to handle sequential or time-dependent data A. Deep Learning does not use training data D. None of the above E. Check Answer Open question page Que: (117). Which statement correctly describes the roles of training, validation, and testing datasets? All three datasets must contain exactly the same records B. Training data is used to learn the model, validation data helps tune or select the model, and testing data evaluates the final model on unseen data A. Testing data is always used to train the model C. Validation data is used only for storing raw data D. None of the above E. Check Answer Open question page Que: (118). Which statement correctly distinguishes overfitting from underfitting? Overfitting occurs when a model learns training data too closely, while underfitting occurs when a model is too simple to capture important patterns A. Underfitting always produces perfect testing accuracy C. Overfitting means the model cannot learn anything from training data B. Overfitting and underfitting have exactly the same meaning D. None of the above E. Check Answer Open question page Que: (119). What is the primary purpose of a confusion matrix in classification? To reduce the number of features in a dataset B. To summarize classification results using measures such as true positives, true negatives, false positives, and false negatives A. To calculate only the training time of a model C. To divide continuous data into clusters D. None of the above E. Check Answer Open question page Que: (120). Which statement correctly describes precision, recall, and F1-score in classification? Precision measures the correctness of positive predictions, recall measures how many actual positives are identified, and F1-score balances precision and recall A. Recall is used only for regression problems C. Precision measures only the training time, while recall measures memory usage B. F1-score is calculated without considering precision or recall D. None of the above E. Check Answer Open question page Que: (121). Which statement correctly describes Mean Squared Error (MSE)? It measures only the number of correctly classified classes B. It is used exclusively to construct a confusion matrix C. It measures prediction error by calculating the average of the squared differences between actual and predicted values A. It ignores the difference between actual and predicted values D. None of the above E. Check Answer Open question page Que: (122). What is Natural Language Processing (NLP)? A method used only for database management C. A technique used only for image processing A. A branch of AI that enables computers to understand and process human language B. A hardware technology for speech generation D. None of the above E. Check Answer Open question page Que: (123). What is tokenization in Natural Language Processing? Converting text into images A. Breaking text into smaller units such as words or sentences C. Removing all meaningful words from a document B. Encrypting a text document D. None of the above E. Check Answer Open question page Que: (124). What is the main purpose of stop-word removal in NLP? To convert text into an image C. To remove commonly occurring words that may carry little useful information A. To translate text into another language B. To increase the size of the vocabulary D. None of the above E. Check Answer Open question page Que: (125). What is the basic idea behind TF-IDF in Natural Language Processing? It measures the importance of a word in a document relative to a collection of documents A. It converts speech directly into video B. It is used only for image classification C. It permanently removes every repeated word from a document D. None of the above E. Check Answer Open question page Que: (126). What is sentiment analysis? The process of compressing an image B. The process of designing a computer network C. The process of identifying the emotional or opinion-related tone of text A. The process of converting source code into machine code D. None of the above E. Check Answer Open question page Que: (127). What is Computer Vision? A branch of AI that enables computers to interpret and analyse visual information A. A programming language for graphics C. A method used only for storing images B. A technique used only for compressing audio D. None of the above E. Check Answer Open question page Que: (128). What is the difference between image classification and object detection? Classification identifies the category of an image, while object detection identifies objects and their locations A. Classification always requires text, while detection requires audio B. Both techniques are used only for speech recognition C. Object detection cannot process images D. None of the above E. Check Answer Open question page Que: (129). What is Optical Character Recognition (OCR)? A technique for converting text present in images or scanned documents into machine-readable text A. A method used only for detecting network attacks C. A technique for converting text into encrypted audio B. A technique for creating database tables D. None of the above E. Check Answer Open question page Que: (130). Which of the following is a major application of Artificial Intelligence in healthcare? Only physical transportation of patients C. Only manual record keeping B. Medical image analysis and disease prediction A. Only spreadsheet formatting D. None of the above E. Check Answer Open question page Que: (131). What is Generative AI? AI that can generate new content such as text, images, audio, or code from learned patterns A. A system used exclusively for network routing C. AI that can only store files without processing them B. A computer hardware component used for memory management D. None of the above E. Check Answer Open question page
