100 Computer MCQs for Competitive Exams

Introduction

In this blog/article, you will learn about some important terminologies, and computer topics frequently asked in BTET and UGC NET exams. There are many optimization strategies to boost your teaching, which this article will outline.

 Assignment/Practice Papers for Class 4-11 Students

Introduction

In this article/blog, students in classes 4 to 11 will learn about some simple computer questions frequently asked in school exams, presented as MCQs and short-answer questions.

Computer 

   Class-4       Class-5        Class-6        Class-7        class-8

  Class-9      Class-10

Session-2024-25 Semester-1

 Class-4     Class-5   Class-6   Class-7   Class-8    Class-9    Class-10

Second term

Class-4 WS1      Class-6 ws1      Class-6 ws2      Class-8 ws1

Class-5 ws1         Class-7 ws1     Class-9 ws1        Class-10 ws1

Science (Class-8)

Worksheet-1    Worksheet-2     Worksheet-3  Worksheet-4

Worksheet-5

Maths/Science (Class-10)

FA-1       FA-2

Computer/IT for class 11 Students

Introduction

In this article, you will be able to know and learn about some essential terminologies, MCQ, and Q&A that are frequently asked while learning and teaching class 11 computer science. The article includes Computer Systems (Chapter 1), Encoding Schemes And Number System (Chapter 2), Emerging Trends (Chapter 3), Introduction To Problem-Solving (Chapter 4), Getting Started with Python (Chapter 5), Flow of Control (Chapter 6), Functions (Chapter 7), Strings (Chapter 8), Lists (Chapter 9), Tuples and Dictionaries (Chapter 10), and Societal Impact (Chapter 11).

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Computer

A computer is an electronic device programmed to accept data (input), process it, and generate results (output).

CPU

·       CPU stands for Central Processing Unit.

·       CPU is also known as a microprocessor.

·       CPU is commonly called processor.

·       CPU is usually referred to as the brain of the computer.

·       CPU is the electronic circuitry of a computer that carries out the actual processing.

·       A CPU can be placed on one or more microchips called IC.

CU

·       CU stands for Control Unit.

·       CU controls sequential instruction execution, interprets instructions, and guides data flow through the computer’s memory, ALU, and input or output devices.

Machine language

Machine language or low-level language refers to the language of 0’s and 1’s that computers understand.

Programming language

Programming language refers to the language used to specify a set of instructions to the computer.

Program

A program refers to an ordered set of instructions executed by a computer to carry out a specific task.

Source code

Source code refers to a program written in a high-level language.

 Compiler

·       A compiler is a unique program that translates the entire source code into object code or machine code or byte code.

·       A compiler, after scanning the whole program, generates error messages, if any.

Interpreter

·       An interpreter is a computer that directly executes instructions into machine language so that they can be understood by the computer.

·       An interpreter processes the program statements one by one, first translating and then executing. This process is continued until an error is encountered or the whole program is executed successfully.

·       The Python interpreter is also called Python shell.

Python

·       Python is a high-level programming language.

·       Python is a free and open-source language.

·       Python is an interpreted language which means Python programs are executed by an interpreter.

·       Python programs are easy to understand.

·       Python programs have a clearly defined syntax and relatively simple structure.

·       Python is portable and platform-independent which means it can run on various operating systems and hardware platforms.

·       Python has a rich library of predefined functions.

·       Python uses indentation for blocks and nested blocks.

Comments

·       Python comments refer to statements used to add a remark or a note in the source code.

·       Comments are not executed by the interpreter.

·       Comments are added with the purpose of making the source code easier for humans to understand.

Keywords

Keywords are reserved words and have a specific meaning to the Python interpreter.

Identifiers

Identifiers refer to names used to identify a variable and function.

Variable

·       Variable in Python refers to an object (an item or element stored in the memory).

·       Value of a variable can be a string, numeric, or any combination of alphanumeric characters.

Data type

·       Data type identifies the type of data values a variable can hold and the operations that can be performed on that data.

·       Every value belongs to a specific data type in Python.

Number data type

·       Number data type stores numerical values only.

·       Number data type is classified into three different types int, float, and complex.

Python sequence

·       Python sequence refers to an ordered collection of items, where each item is indexed by an integer.

·       The three types of sequence data types available in Python are strings, lists, and tuples.

Strings

·       Strings refer to a group of characters. These characters may be alphabets, digits, or special characters including spaces.

·       String values are enclosed either in single or double quotation marks.

List

A list refers to a sequence of items separated by commas and the items are enclosed in square brackets.

Tuple

·       Tuple is a sequence of items separated by commas and items are enclosed in parenthesis ().

·       Tuple once created, cannot be changed.

 Set

·       Set refers to an ordered collection of items separated by commas and the items are enclosed in curly brackets { }.

·       A set cannot have duplicate entries.

·       Elements of a set once created, cannot be changed.

None

·       None is a special data type with a single value.

·       None is used to signify the absence of value in a situation.

·       None supports no special operations, and it is neither the same as False nor 0 (zero).

Mapping

·       Mapping refers to an unordered data type in Python.

·       Dictionary is the only standard mapping data type in Python.

Dictionary

·       Dictionary in Python holds data items in key-value pairs.

·       Dictionaries permit faster access to data.

·       Items in a dictionary are enclosed in curly brackets {}.

·       The key: value pairs of a dictionary can be accessed using the key.

·       Every key is separated from its value using a colon (:) sign.

Mutable variables

·       Mutable variables refer to variables whose values can be changed after they are created and assigned.

·       Some mutable data types are lists, sets, and dictionaries.

Immutable variables

·       Immutable variables refer to variables whose values cannot be changed after they are created and assigned.

·       Some immutable data types are Integers, Float, Boolean, Complex, Strings, and Tuples.

Operand

Operands refer to the values that the operators work on.

Immutable string

An immutable string means that the content of the string cannot be changed after it has been created.

Catenation

Catenation refers to the joining of two strings by using the ‘+’ symbol.

Repetition

Repetition means repeating the given string by using the ‘*’ symbol.

Slicing

In Python, slicing is used to access some parts of the string and sub-string.

Module

A module is a grouping of functions.

String

·       A string is a sequence that is made up of one or more Unicode characters.

·       In Python, a string can be created by enclosing one or more characters in single, double, or triple quotes.

Flow of execution

·       Flow of execution refers to the order in which the statements in the program are executed.

·       The Python interpreter starts executing the instructions in a program.

Scope of variable

The variable's scope refers to the part of the program where the variable is accessible.

Global variable

A global variable refers to a variable that has global scope.

Local variable

·       Local variable refers to a variable that has local scope.

·       A local variable is defined inside any function.

Built-in-function

Built-in functions are the readymade Python functions frequently used in programs.

Function

·       Function is a named group of instructions that can accomplish a specific task when it is invoked.

·       A function can be called repeatedly from different places in the program.

 Argument in function

An argument in a function is a value passed to the function during the function call which is received in the corresponding parameter defined in the function header.

Default parameter

A default parameter or value refers to a value that is pre-decided and assigned to the parameter when the function called doesn’t have its corresponding argument.

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  Computers for Class 5 Kids

Introduction

In this article, you will be able to know and learn about some important terminologies, and MCQs that are frequently asked in class-5.

 

MCQ Set-1

MCQ Set-2

MCQ Set-3

MCQ Set-4

class-5 final assignment

 Computers for Class 4 Kids

Introduction

In this article, you will learn about some important terminologies and MCQs frequently asked in class 4.

Computer

A computer is a fast electronic data processing machine.

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9 Lines on Computer

Hardware

Hardware refers to the physical parts of a computer that we can touch or feel.

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Software

Software is the set of programs or instructions that tell the hardware what to do.

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Input devices

Input devices are the devices through which we enter data and instructions into the computer.

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6 lines on input devices

7 lines on output devices

MCQ Set-1

MCQ Set-2

MCQ Set-3

MCQ Set-4

Session 2024-25

Set-1

Assignment for final exam

 The UGC NET Computer Science (Artificial Intelligence)

Introduction

In this article, you will know and learn about some important terminologies, and MCQs frequently asked in the UGC NET Computer Science.

Artificial Intelligence (AI)

·       John Mc Carthy coined the term AI in the 1950s.

·       AI is a branch of computer science.

·       AI is the simulation of human intelligence in machines programmed to think like humans and mimic their actions.

·       AI is used to create intelligent machines that can behave like humans, think like humans, and make decisions under different situations.

Artificial Intelligence (AI)

·       AI is a broad term referring to robots, bots, chatbots, androids, and cyborgs.

·       AI is a field of computer science that promotes the creation of intelligent machines that work and react like humans.

·       AI is a field of study that tries to make computers smart.

·       AI is the ability of a computer program or a machine to think and learn.

·       Some of the activities that computers with AI are designed for include speech recognition, learning and planning, and problem-solving.

·       The five basic components of AI are learning, reasoning, problem-solving, perception, and language understanding.

Agent

·       Agent is just something that perceives and acts.

·       An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors.

·       An AI system is composed of an agent and its environment.

·       In the AI system, our aim is to design agents.

·       The four main agent types are Artists, sales, distributors, and licensing agents.

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Rational Agent in AI

·       Rational Agent in AI refers to a theoretical entity that considers realistic models of how people think, with preferences for advantageous outcomes and an ability to learn.

·       A Rational Agent in AI could be anything that makes decisions, such as a person, firm, machine, or software.

·       A Rational Agent in AI carries out an action with the best outcome after considering past and current percepts.

·       A Rational Agent performs the actions that cause the agent to be most successful.

·       Rational Agent is a computer program that performs tasks based on pre-defined rules and procedures.

Intelligent agent

·       An intelligent agent is a system that can perceive its environment and take actions to achieve a specific goal.

·       An intelligent agent is an advanced computer system that can gather, analyze, and respond to the data it collects from its surrounding environment.

·       An intelligent agent can be a self-driving car or a virtual personal assistant.

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7 types of AI environment (Part-1)    

AI Environment (Part-2)

Machine Learning (ML)

·       ML is a branch of AI and computer science.

·       ML works on using data and algorithms to imitate how humans learn to improve the machine’s accuracy.

·       ML allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.

·       ML is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

·       ML allows systems to automatically learn and improve from experience without being explicitly programmed.

·       ML depends on mathematics and statistics. Understanding of some mathematical and statistical methods is required to modify ML models or to build/create new models.

Natural language processing (NLP)

·       NLP is a collective term referring to the automatic computational processing of human languages.

·       NLP is a subfield of linguistics, computer science, and AI concerned with the interactions between computers and human languages.

·       NLP refers to the branch of AI that gives computers the ability to understand text and spoken words in much the same way human beings can.

·       NLP helps a computer communicate with humans in their own language and scales other languages–related tasks.

·       NLP allows computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are important.

Neural network

·       Neural network is also known as artificial neural network (ANN) or simulated neural network (SNN).

·       A neural network refers to a series of algorithms that endeavor to recognize underlying relationships in a data set through a process miming how the human brain operates.

·       Neural networks are networks used in ML that work like the human nervous system.

Supervised ML

·       Supervised ML is the ML task of learning a function that maps an input to an output based on example input-output pairs.

·       Supervised ML uses past data to make predictions.

·       Supervised ML uses classification and regression ML algorithms.

·       A common example of supervised ML is the spam filtering of e-mails.

Unsupervised ML

·       Unsupervised ML refers to a type of algorithm that learns patterns from untagged data.

·       Unsupervised ML deals with unlabelled data.

·       Unsupervised ML finds hidden patterns. Through mimicry, the machine is forced to build a compact internal representation of its world and generate imaginative content.

·       Unsupervised ML allows the model to work on its own to discover patterns and information that was previously undetected.

·       Unsupervised ML uses clustering and association ML algorithms.

·       A common example of unsupervised ML is Facebook.

Reinforcement ML

·       Reinforcement learning is a type of ML method where an intelligent agent (computer program) interacts with the environment and learns to act within that.

·       Reinforcement learning in ANN is a goal-directed computational approach where an agent learns to perform a task by interacting with an unknown dynamic environment.

·       Reinforcement learning is about an autonomous agent taking suitable actions to maximize rewards in a particular environment.

·       Reinforcement learning is an ML training method based on rewarding desired behaviors and/or punishing undesired ones. During learning, the learning algorithm updates the agent policy parameters.

·       Some common examples of NLP that use reinforcement learning are predictive text, text summarization, question answering, and machine translation.

·       By studying typical language pattern reinforcement learning agents can mimic and predict how people speak to each other every day.

·       Reinforcement ML is used for improving or increasing efficiency.

Perceptrons

Perceptrons are two-layer networks with one input and one output.

Single-Layer Perceptron (SLP)

·       SLP stands for single-layer perceptron.

·       SLP is the simplest type of ANN.

·       A SLP is a feed-forward network based on a threshold transfer function.

·       A SLP can only learn linear functions.

·       SLP can classify only linearly separable cases with a binary target (1,0).

Multilayer perceptron(MLP)

·       MLP can learn non-linear functions.

·       A MLP contains one or more hidden layers (apart from one input and one output layer).

Space

In an AI problem, space refers to the exhaustive collection of all conceivable states.

State

·       AI problems can be represented as a set of well-formed states.

·       A state can be an initial state or a goal state.

State space

·       State space is known as the set of all possible and known states of a system.

·       In state space, each unique point represents a state of the system. For example, take a pendulum moving into and fro motion. The state of such an idealized pendulum is represented by its angle and its angular velocity.

·       Four state space forms are the phase variable form (controller form), the observer form, the modal form, and the Jordan form.

State space representation

·       State space representation is a mathematical model of a physical system expressed as a function of input, output, and state variables related by first-order differential equations or difference equations.

·       The state of the system can be represented as a vector within that space.

State-space models

·       State-space models use state variables to describe a system by a set of first-order differential or difference equations.

State space representation of the problem

State space representation of the problem refers to a set of all possible states for a given problem. For example, chess game, the initial position of all the pieces on a chess board defines the initial state. 

Knowledge representation in AI

·       Knowledge representation in AI is a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning.

·       Knowledge representation in AI describes the representation of knowledge.

·       The four main approaches to knowledge representation in AI are simple relational knowledge, inheritable knowledge, inferential knowledge, and procedural knowledge.

Semantic networks

·       Semantic networks are a logic-based formalism for knowledge representation.

·       A Semantic network is a method of knowledge representation that represents semantic relations between concepts using a directed or undirected graph consisting of vertices (indicating concepts) and edges (including relations).

·       Semantic networks in AI are graphical structures designed to represent and organize knowledge, enabling machines to understand and process information in human-readable form.

·       Semantic networks are a way of representing relationships between objects and ideas.

·       Semantic networks consist of nodes representing concepts or objects and links denoting relationships between them.

·       For example, a network might tell a computer the relationship between different animals (a cat 1’s a mammal, and a cat has whispers).

·       A common example of a semantic network is WordNet, a lexical database of English.

Knowledge representation in logic

·       Knowledge representation in logic gives processable form to all the information that can be precisely expressed in any other language. Logic allows the expression of all the information stored in computer memory.

·       Three modern roles for logic in AI, which are based on the theory of tractable Boolean circuits are a) logic as a basis for computation b) logic for learning from a combination of data and knowledge c) logic for reasoning about the behavior of ML systems.

·       A common example of logical representation is, we can represent “Richard is a king, Jack is a person, and all kings are persons”. Using the following predictive logic: Richard is a king.

Ontology-based Knowledge Representation

·       Ontology-based knowledge representation describes the individual instances and roles in the domain that are represented using unary and binary predicates.

·       Ontology-based Knowledge representation enables knowledge sharing, processing, reuse, capturing, and communication.

·       Ontology seeks the classification and explanation of entities.

Planning

·       Planning is the process of determining various actions that often lead to a solution.

·       Planning is considered the logical side of acting.

·       The entire process of the planning system includes missions, objectives, policies, procedures, programs, budgets, and strategies.

·       The three main components of planning are strategic thinking, long-range planning, and operational planning.

Planning in AI

· Planning in AI is about the decision-making tasks performed by robots or computer programs to achieve a specific goal.

·       The execution of planning is about choosing a sequence of activities with a high likelihood of completing the specific tasks.

Different types of planning in AI are

·       Classical planning

·       Reduction to other problems

·       Temporal planning

·       Probabilistic planning

·       Preference-based planning

·       Conditional planning

·       Contingent planning

·       Conformant planning

·       FSSP (Forward State Space Planning)

·       BSSP (Backward State Space Planning)

FSSP (Forward State Space Planning)

FSSP says that given an initial state in any domain, we perform some necessary actions and obtain a new state S (which also contains some new terms), called a progression. It continues until we reach the target position.

BSSP (Backward State Space Planning)

In BSSP, we move from the target state g to the sub-goal g, tracing the previous action to achieve that goal. This process is called regression (going back to the previous goal or sub-goal). These sub-goals should also be checked for consistency.

Goal Stack Planning (GSP) in AI

·       GSP is a method in which we work backward from the goal state to the initial state. We make use of a stack to hold these goals that need to be fulfilled as well as the actions that we need to perform for the same.

·       GSP is designed to handle problems having compound goals.

·       GSP utilizes STRIP as a formal language for specifying and manipulating the world with which it is working.

Turing test

·       The Turing test is named after Alan Turing.

·       Turing test is a method of inquiry in AI for determining whether or not a computer is capable of thinking like a human being.

·       A Turing test requires humans, interrogators, and a computer.

·       Turing machines provide a powerful computational model for solving problems in computer science and testing the limits of computation.

·       Turing machines are similar to finite automata/finite state machines but have the advantage of unlimited memory.

·       A common example of Turing is the interview process for Turing.

CAPTCHA

·       CAPTCHA stands for Completely Automated Public Turing Test.

·       CAPTCHA is an interactive feature added to web forms to distinguish whether a human or automated agent is using the form.

Rational agent approach

·       A rational approach is also called a rational choice theory or rational action theory or choice theory.

·       Acting rationally means acting to achieve one’s goals, given one’s beliefs.

Script

A script is a structured representation describing a stereotyped sequence of events in a particular context.

Frames

·       A frame is also known as slot-filter knowledge representation in AI.

·       Frames are the AI data structure that divides knowledge into substructures by representing stereotyped situations.

·       A frame is a sequence of bits consisting of the frame coordination bits such as a frame check sequence and the packet payload.

·       Frame-based systems contain the ideas of classes, instances, and inheritance. For example, the class vehicle could be defined, along with the subclasses car and truck.

·       A frame consists of a collection of slots and slot values.

·       The two main categories of frame structures are braced frame structure and rigid frame structure.

·       Some common examples of frames are V.42 modem frames, Fibre channel frames, PPP (Point-to-point) frames, and Ethernet frames.

Linear planning

·       Linear planning is the planning or scheduling of project management tasks where distance is a significant factor in the project. Some common examples of projects include roads, rail, pipelines, and transmission lines.

·       Linear planning considers the time factors of a task as well as the location factors.

·       Linear models are supervised learning algorithms used for solving either classification or regression problems.

Non-linear planning

·       Non-linear planning is used to set a goal stack and is included in the search space of all possible sub-goal orderings.

·       Non-linear planning handles the goal interactions by the interleaving method.

·       Non-linear planning takes a larger search space since all possible goal orderings are considered.

·       A common example of non-linear planning in AI is “start the car and put on the Bluetooth, then complete task 2 of a phone call, and then finally, complete task 1 by leaving the car at the service station”.

Linear programming

Linear programming is a method to achieve the best outcome in a mathematical model whose requirements are represented by linear relationships.

Non-linear programming

Non-linear programming is a process of solving an optimization problem where the constraints or the objective functions are non-linear.