AI and ML Quiz practice set 04th October 2026 Share On Que: (1). What does Artificial Intelligence primarily aim to develop? Systems capable of performing tasks requiring human-like intelligence A. Systems that can only store large amounts of data C. Systems used only for numerical calculations B. 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 make all computer programs deterministic C. To eliminate the need for computer hardware B. To enable machines to learn, reason and make decisions intelligently A. 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. Learning A. Compilation B. 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? Computer Science A. Civil Engineering B. 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? Natural Language Processing A. Robotics C. Computer Vision B. 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 requires every possible rule to be manually programmed B. It enables systems to learn patterns from data and improve performance A. 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 enable computers to understand and process human language A. To design computer processors B. 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 enable robots to perceive their environment and perform intelligent actions A. To increase only the storage capacity of computers C. To replace all computer networks B. 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 increase the clock speed of processors B. To enable computers to interpret and understand visual information A. To manage database transactions C. 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 that uses a knowledge base and inference mechanism to solve problems in a specific domain A. A computer system without any predefined knowledge C. A system used only for storing multimedia files B. 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? Artificial Intelligence is a subset of Machine Learning B. Both terms always have exactly the same meaning C. Machine Learning is a subset or approach within Artificial Intelligence A. 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? 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. Artificial Intelligence is a subset of Deep Learning B. 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? Complete elimination of human supervision; zero implementation cost C. No requirement for data; unlimited accuracy B. Fast data processing and decision support; dependence on data and computational resources A. 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. A device used exclusively for data storage C. An entity that perceives its environment and acts upon it to achieve objectives A. 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? Compiler, interpreter and assembler agents B. Simple reflex, model-based reflex, goal-based, utility-based and learning agents A. Analog, digital and hybrid processors C. 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 selects actions based on the current percept using condition-action rules A. It always maintains a complete history of the environment B. 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 cannot perceive its environment B. A model-based reflex agent never uses previous percept information C. 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 selects actions by considering whether they help achieve a specified goal A. It does not require any information about the environment C. It can only respond using fixed condition-action rules B. 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 identify only the physical sensors of an agent C. To convert machine code into source code B. To measure the desirability or usefulness of possible outcomes A. 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, Execution, Algorithm and Storage C. Performance measure, Environment, Actuators and Sensors A. 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 specify only the physical dimensions of the agent B. To evaluate how successfully an agent is achieving its objectives A. To determine the programming language used by the agent C. 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 enable the agent to perform actions on the environment A. They store the complete history of all percepts C. 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 physically modify the environment B. To execute the agent's program instructions C. 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 storing all possible solutions in memory C. 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. 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 only the final solution of a problem B. Representation of computer memory locations only C. 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 set of all possible operators C. The condition that always represents the final solution B. The starting condition from which the search begins A. 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? The first state generated by a search algorithm B. A state that satisfies the conditions specified by the problem objective A. A state that can never be reached from the initial state C. 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? Rules or actions that transform one state into another A. The final states that terminate every search B. The memory locations used by the search algorithm C. 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 tree structure representing the states generated during the search process A. A data structure used exclusively for sorting numbers C. 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? Only the states that are already part of the final solution C. The set of all possible states that can be considered while solving the problem A. Only the initial state of a problem B. 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 always uses a heuristic function B. A search strategy that does not use problem-specific heuristic information A. 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? Priority queue based only on heuristic value C. Queue A. 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)? Stack A. Queue B. Hash table only C. 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 repeatedly performs depth-limited search with increasing depth limits A. It uses only heuristic information without considering depth C. It performs DFS only once with an unlimited depth B. 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 uses additional problem-specific knowledge to guide the search A. A search strategy that never uses any information about the goal B. 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 uses an estimate of the cost or distance to the goal to guide exploration A. A search technique that always explores states in alphabetical order C. 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? An estimated cost from node n to a goal state A. The exact cost already spent from the initial state to node n B. The total number of nodes in the search tree C. 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 heuristic value h(n) A. The depth of the node only C. The path cost g(n) only B. 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) B. f(n) = g(n) + h(n) A. f(n) = g(n) × h(n) C. 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 repeatedly moves to a neighbouring state that appears better according to an evaluation function A. It always explores every node at the same depth before proceeding B. It always guarantees the globally optimal solution C. 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 method of representing knowledge about the real world in a form that an AI system can use for reasoning A. A technique used only for increasing processor speed C. A method used only to compress computer files B. 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 collection of computer hardware components C. A database containing only multimedia files B. 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 specifies a logical relationship between conditions and conclusions A. A rule is used only to store images B. A rule represents the physical hardware of an AI system C. 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 logic used only for numerical calculations B. 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. 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 can represent only statements with no internal structure B. It can represent objects, properties and relationships between objects A. It cannot use variables or predicates C. 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 network used only for transmitting computer packets B. A database containing only numerical values C. A graph-based representation in which nodes represent concepts and links represent relationships A. 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 network protocol used for communication C. A structured representation used to describe an object or concept using attributes and associated values A. A hardware component used to execute AI algorithms B. 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 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. A method that randomly selects rules without using facts C. 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 method that always starts with all available facts and derives every possible conclusion B. A goal-driven inference method that starts with a goal and works backward to find supporting facts A. 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. An AI system that uses stored domain knowledge and reasoning to solve problems like a human expert A. A computer system that performs only basic arithmetic operations C. 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 must always replace human experts completely B. It can work only without any stored knowledge C. It uses domain-specific knowledge to provide expert-level advice or decisions A. 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 stores only the user interface design B. It applies rules to known facts to derive conclusions A. It is responsible only for collecting sensor data C. 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 store all domain knowledge permanently B. To replace the inference engine C. 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 designing only the graphical user interface C. The process of deleting the inference engine B. 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 can operate only as a database C. An expert system that represents knowledge mainly using IF-THEN rules A. An expert system that contains no knowledge base B. 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? Financial and business decision support C. Fault diagnosis in technical systems B. Medical diagnosis and decision support A. 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. Unlimited general intelligence is an advantage, while low accuracy is always a limitation B. Consistent decision support is an advantage, while knowledge acquisition and maintenance can be difficult A. 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 programming language used to create artificial intelligence programs C. A method used only for designing computer hardware B. A technique in which computers learn patterns from data and improve their performance without being explicitly programmed for every task A. 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? Learning from data A. Manual hardware configuration C. Fixed rule execution B. 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. Only the final output produced by a model C. A collection of data used for analysis, learning, evaluation, or testing of a machine learning system A. 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. An input variable or measurable characteristic used by a model to learn patterns A. The complete training algorithm C. 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 algorithm used to train a model C. A variable used only for identifying the computer B. The known target or desired output associated with an input example A. 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 permanently store the final predictions only B. To replace the learning algorithm C. To enable the model to learn patterns or relationships from examples A. 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 evaluate how well a trained model performs on previously unseen data A. To create the programming language used by the model C. To directly modify the model parameters during training B. 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 physical storage device C. A learned representation or function that maps input data to an output or prediction A. A raw dataset that has not been processed B. 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? Testing data is always used before training data 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 its performance on unseen examples A. 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? Unsupervised Learning B. Reinforcement Learning C. Supervised Learning A. 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? Reinforcement Learning C. Unsupervised Learning B. Supervised Learning A. 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? Unsupervised Learning B. Reinforcement Learning C. 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 assigning input data to one or more predefined classes A. A process of grouping data without any predefined structure B. A process of predicting only continuous numerical values C. 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? Predicting only continuous numerical quantities B. Finding clusters without using any labeled data C. Learning a decision boundary or relationship that separates data into predefined categories A. 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? The algorithm assumes that all features are independent C. A new data point is classified according to the classes of its nearest training examples A. A decision tree is always constructed before classification B. 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 represents decisions using a tree-like structure of tests, branches, and outcomes A. It can only solve unsupervised learning problems C. 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. The dataset must contain no categorical features C. All features must have identical values B. 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 group data randomly into different classes B. To always construct a decision tree C. 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 technique used only to classify images into categories B. A method used exclusively for finding clusters C. A supervised learning technique used to predict continuous numerical values A. 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 learns a relationship between input variables and a continuous output variable A. It works without any training examples C. It always produces only categorical output B. 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 classification technique that always creates decision trees B. A regression technique that models the relationship between variables using a linear function A. A clustering technique based only on distances C. 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 classification algorithm based on nearest neighbors C. A regression model that must contain at least ten independent variables B. A linear regression model involving one independent variable and one dependent variable A. 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 is an unsupervised clustering algorithm C. It predicts a class using only one categorical feature B. It predicts a continuous dependent variable using two or more independent variables A. 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? Both classification and regression always produce identical types of output C. Classification predicts categorical classes, whereas regression generally predicts continuous numerical values A. Classification predicts only continuous values, whereas regression predicts only categories B. 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 predict a known target value from labeled data B. To group similar data objects together without using predefined class labels A. To classify data using predefined labels only C. 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 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 always requires the number of clusters to be fixed before learning B. 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? Predicting only continuous numerical values B. Discovering relationships or co-occurrence patterns among items in a dataset A. 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? Learning only from labeled input-output pairs B. Grouping data without using any feedback C. 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 provides predefined class labels for supervised learning C. The agent observes the environment, selects actions, and learns from the resulting rewards A. 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 measure of the physical size of the environment C. A dataset containing only labeled examples B. A strategy that determines which action an agent should take in a given state A. 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 groups data into clusters using only Euclidean distance C. It requires all training examples to have predefined class labels B. 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 stores the complete training dataset permanently B. It processes input values using weights and an activation function to produce an output A. It only performs database operations C. 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? A database indexing technique C. A basic single-layer neural model that can be used for binary classification A. An unsupervised clustering algorithm B. 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 hidden layer only stores the original dataset B. 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. 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 divide a dataset into training and testing sets C. To permanently store training data B. To introduce non-linearity and determine the output of a neuron based on its input A. 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? 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. All three datasets must contain exactly the same records B. 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 only the training time, while recall measures memory usage B. Recall is used only for regression problems C. Precision measures the correctness of positive predictions, recall measures how many actual positives are identified, and F1-score balances precision and recall A. 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 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 measures only the number of correctly classified classes B. 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 technique used only for image processing A. A method used only for database management C. 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? Breaking text into smaller units such as words or sentences C. Removing all meaningful words from a document B. Converting text into images A. 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 translate text into another language B. To convert text into an image C. To remove commonly occurring words that may carry little useful information A. 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 identifying the emotional or opinion-related tone of text A. The process of designing a computer network C. The process of compressing an image B. 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 programming language for graphics C. A method used only for storing images B. A branch of AI that enables computers to interpret and analyse visual information A. 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 always requires text, while detection requires audio B. Classification identifies the category of an image, while object detection identifies objects and their locations A. 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 method used only for detecting network attacks C. A technique for converting text into encrypted audio B. A technique for converting text present in images or scanned documents into machine-readable text A. 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 manual record keeping B. Medical image analysis and disease prediction A. Only physical transportation of patients C. Only spreadsheet formatting D. None of the above E. Check Answer Open question page Que: (131). What is Generative AI? AI that can only store files without processing them B. 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. A computer hardware component used for memory management D. None of the above E. Check Answer Open question page
