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 capable of performing tasks requiring human-like intelligence A. Systems that can only store large amounts of data C. 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 machine to perform tasks that normally require human intelligence A. The ability of a computer to store unlimited data B. 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 enable machines to learn, reason and make decisions intelligently A. To eliminate the need for computer hardware B. 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? Learning A. Compilation B. Formatting C. 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. Mechanical Engineering C. Computer Science A. 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. Computer Vision B. 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 is concerned only with computer hardware design C. It requires every possible rule to be manually programmed B. 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 manage physical network cables C. To enable computers to understand and process human language A. To design computer processors B. 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 increase only the storage capacity of computers C. To enable robots to perceive their environment and perform intelligent actions A. 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 manage database transactions C. To increase the clock speed of processors B. To enable computers to interpret and understand visual information A. 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 computer system without any predefined knowledge C. A system used only for storing multimedia files B. 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? Both terms always have exactly the same meaning C. Machine Learning is a subset or approach within Artificial Intelligence A. 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? Deep Learning and AI are completely unrelated fields C. 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. 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. No requirement for data; unlimited accuracy B. Complete elimination of human supervision; zero implementation cost C. 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? An entity that perceives its environment and acts upon it to achieve objectives A. A device used exclusively for data storage C. A computer program that can only perform arithmetic operations B. 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 is receiving information from the environment, while action is the agent response to that information A. Both perception and action refer only to internal memory operations C. Perception means changing the hardware and action means storing data B. 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 always selects a random action B. An agent that never interacts with its environment C. 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 selects actions based on the current percept using condition-action rules A. It always maintains a complete history of the environment B. It requires a utility function for every decision C. 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 never uses previous percept information C. A model-based reflex agent cannot perceive its environment B. A model-based reflex agent maintains an internal state representing aspects of the environment A. 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 does not require any information about the environment C. It selects actions by considering whether they help achieve a specified goal A. 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 convert machine code into source code B. To measure the desirability or usefulness of possible outcomes A. 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? Inability to respond to environmental changes C. Autonomy and the ability to perceive and act in an environment A. Complete dependence on continuous human instructions B. 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? Performance measure, Environment, Actuators and Sensors A. Program, Environment, Algorithm and Software B. 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 determine the programming language used by the agent C. To specify only the physical dimensions of the agent B. To evaluate how successfully an agent is achieving its objectives A. 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? Only the internal memory of an agent B. The source code of the agent program C. 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 are used only to collect information from the environment B. They store the complete history of all percepts C. They enable the agent to perform actions on the environment A. 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 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 computer memory locations only C. Representation of a problem using possible states and transitions between those states A. Representation of only the final solution of a problem B. 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 satisfies the conditions specified by the problem objective A. The first state generated by a search algorithm B. 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? The memory locations used by the search algorithm C. The final states that terminate every search B. Rules or actions that transform one state into another A. 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? Only the initial state of a problem B. The set of all possible states that can be considered while solving the problem A. 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 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? Queue A. Stack B. Priority queue based only on heuristic value C. 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. Stack A. 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 always searches the entire state space B. It uses only heuristic values to select nodes C. 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 repeatedly performs depth-limited search with increasing depth limits A. 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 never uses any information about the goal B. A search strategy that examines states in completely random order C. A search strategy that uses additional problem-specific knowledge to guide the search A. 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 uses an estimate of the cost or distance to the goal to guide exploration A. A search technique that never evaluates generated states B. 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? The exact cost already spent from the initial state to node n B. An estimated cost from node n to a goal state A. 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 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) B. 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 always explores every node at the same depth before proceeding B. It always guarantees the globally optimal solution C. 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 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 procedure used to execute a computer program C. A statement that represents information known to be true A. 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 formal logic in which statements are represented as propositions that can be true or false A. A programming language used to create operating systems C. 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 objects, properties and relationships between objects A. It can represent only statements with no internal structure B. 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 database containing only numerical values C. A graph-based representation in which nodes represent concepts and links represent relationships A. 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 network protocol used for communication C. 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. 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 starts only with a goal and works backward B. 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. 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 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. A method used only for sorting data C. 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? An AI system that uses stored domain knowledge and reasoning to solve problems like a human expert A. A system used only for storing large amounts of numerical data B. 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 uses domain-specific knowledge to provide expert-level advice or decisions A. It can work only without any stored knowledge C. 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 provide the physical hardware required by the system B. To display the graphical interface only C. 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 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 obtaining and incorporating knowledge from experts and other sources into the knowledge base A. The process of designing only the graphical user interface C. 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 contains no knowledge base B. 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? Unlimited general intelligence is an advantage, while low accuracy is always a limitation B. 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. 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. Learning from data A. 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 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 single instruction given to a computer B. 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? An input variable or measurable characteristic used by a model to learn patterns A. The complete training algorithm C. The final decision made by the model B. 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 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 evaluate how well a trained model performs on previously unseen data A. 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 raw dataset that has not been processed B. A physical storage device C. A learned representation or function that maps input data to an output or prediction A. 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 writing source code manually C. The process of collecting raw data only B. The output or estimated result produced by a trained model for given input data A. 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. Testing data is always used before training data B. Training and testing data must always contain exactly the same records C. 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 manually writing every decision rule B. The process of adjusting or estimating model parameters from data to improve performance on a task A. The process of converting software into hardware C. 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 to memorize every training example exactly B. The process of increasing the size of the computer memory C. The ability of a trained model to perform well on new, previously unseen data A. 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? Supervised Learning A. Reinforcement Learning C. 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? Unsupervised Learning B. Supervised Learning A. Reinforcement Learning C. 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 a combination of a small amount of labeled data and a larger amount of unlabeled data A. It uses only unlabeled data C. 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? 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 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 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 always requires all features to be statistically independent B. It represents decisions using a tree-like structure of tests, branches, and outcomes A. It can only solve unsupervised learning problems C. 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? The dataset must contain no categorical features C. Features are assumed to be conditionally independent given the class A. 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 group data randomly into different classes B. To find a decision boundary that separates classes while maximizing the margin between them A. 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 estimate the probability of an observation belonging to a class A. To construct only hierarchical clusters B. To find the shortest path in a graph C. 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 technique used only to classify images into categories B. A method used exclusively for finding clusters C. 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 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 classification algorithm based on nearest neighbors C. 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 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? Both classification and regression always produce identical types of output C. Classification predicts only continuous values, whereas regression predicts only categories B. Classification predicts categorical classes, whereas regression generally predicts continuous numerical values A. 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? 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 always used to train the model B. 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 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 never requires labeled data B. 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 divides data into a specified number of clusters by assigning observations to the nearest cluster centroid A. It requires every training example to have a predefined class label B. It always creates a hierarchical tree of clusters C. 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 always requires the number of clusters to be fixed before learning B. 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? Assigning every observation to a predefined class C. Discovering relationships or co-occurrence patterns among items in a dataset A. Predicting only continuous numerical values B. 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 reduce the dimensionality of data while retaining as much important variation as possible A. To assign predefined class labels to data B. 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 strategy that determines which action an agent should take in a given state A. A dataset containing only labeled examples B. 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 always repeat the first action it learns C. The agent must balance trying new actions with choosing actions that are already known to provide good rewards A. 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 processes input values using weights and an activation function to produce an output A. It stores the complete training dataset permanently B. 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? An unsupervised clustering algorithm B. A database indexing technique C. A basic single-layer neural model that can be used for binary classification A. 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 output layer always contains the raw input data C. The input layer receives data, hidden layers perform intermediate processing, and the output layer produces the final result A. 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 divide a dataset into training and testing sets C. To introduce non-linearity and determine the output of a neuron based on its input A. 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. 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. CNNs and RNNs are database management algorithms C. 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? Testing data is always used to train the model C. 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. 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? Underfitting always produces perfect testing accuracy C. Overfitting occurs when a model learns training data too closely, while underfitting occurs when a model is too simple to capture important patterns A. 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? 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. 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 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? Converting text into images A. Removing all meaningful words from a document B. Breaking text into smaller units such as words or sentences C. 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 is used only for image classification C. It converts speech directly into video B. It measures the importance of a word in a document relative to a collection of documents A. 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 method used only for storing images B. A branch of AI that enables computers to interpret and analyse visual information A. A programming language for graphics C. 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? Both techniques are used only for speech recognition C. 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. 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 present in images or scanned documents into machine-readable text A. 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 manual record keeping B. Only physical transportation of patients C. 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 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
