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Computer

Computer MCQs

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Advance Html

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Advanced Mapreduce Concepts

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Advanced Oop Concepts

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Advanced Sql

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Advanced Topics In R Programming

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Advanced Topics In Ruby

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Advanced Trees (Avl, Redblack, Btrees)

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Animation

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Application Lifecycle And Annotation

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Applications Of Data Structures

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Array And Function

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Arrays

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Arrays And Strings

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Arrays And Strings In C Plus Plus

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Arrays And Strings In C Sharp

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Arrays In Data Structures

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Arrays, Hashes And Strings In Ruby

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Asp.Net And Web Development With C Sharp

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Basic And Variables

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Basic Css

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Basic Html

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Basic Php

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Basic Syntax And Data Types In C Sharp

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Basic Syntax In Ruby

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Best Practices And Code Conventions In Ruby

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Best Practices And Optimization Of Hadoop

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Big Data And Distributed Computing

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Binary Search Trees(B Tree)

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Bitwise And Boolean

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Blocks And Procs In Ruby

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Caching And Debugging

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Cgi Programming And Miscellaneous

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Classes And Modules

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Classes And Objects In C Plus Plus

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Classes And Objects In C Sharp

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Classes And Objects In Python

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Classes And Objects In Ruby

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Client Server Database

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Collections Framework In Java

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Computer

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Computer Fundamental Miscellaneous

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Concept Of Object Oriented Programs In Python

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Conditional Formatting (While And For Loops)

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Constructors And Destructors In C Plus Plus

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Constructors And Methods

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Control Flow Statements In C Plus Plus

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Control Flow Statements In C Sharp

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Control Structures

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Control Structures In R Programming

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Control Structures In Ruby

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Control Structures(Loop In Php)

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Css Layout And Images

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Css Lists, Tables And Forms

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Css Properties, Css Elements, Css Functions And Tables

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Css Selectors And Css Files

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Css Text, Borders And Images

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Css3 Fundamentals

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Css3 Transitions And Css 3 Animations

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Data Analysis With Python

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Data And Database Administration

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Data Collection And Preprocessing

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Data Ethics And Privacy

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Data Ingestion With Sqoop And Flume

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Data Integration And Management With Apache Nifi

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Data Manipulation With Dplyr

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Data Modeling

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Data Modeling With Er Model

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Data Models Into Database Designs

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Data Processing With Pig

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Data Science Capstone Project

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Data Science In Business

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Data Science Miscellaneous

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Data Science Tools And Frameworks

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Data Structures In R Programming

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Data Types And Numeric Types

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Data Types And Variables

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Data Types And Variables In Ruby

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Data Visualization With Ggplot2

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Data Warehousing

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Database Connectivity With Python

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Database Design Using Normalization

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Database Processing For Bis

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Database Redesign

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Databases Handling

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Date And Timestamp

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Declaration And Access Control

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Decorators In Depth

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Delegates And Events In C Sharp

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Developing Mapreduce Applications

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Dictionary In Python

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Distributed Databases

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Document Object Model And Event Handling

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Dynamic Programming In Data Structures

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Eigrp And Ospf

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Emerging Trends In Data Science

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Enumerators And Iterators In Ruby

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Er Model And Business Rules

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Event Handling In Java

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Exception Handling In C Plus Plus

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Exception Handling In C Sharp

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Exception Handling In Python

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Exception Handling In Ruby

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Exceptions

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Exceptions And Error Handling

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Exploratory Data Analysis (Eda)

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File Handling And Input Output In C Sharp

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File Handling In C Plus Plus

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File Input Output

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File Input Output In Ruby

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File System

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Files Handling In Python

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Flow Control

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Formatting And Decorators

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Function

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Functions

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Functions And Methods In C Sharp

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Functions And Procedures In C Plus Plus

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Functions In Python

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Fundamentals

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Generators And Iterators

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Generics In Java

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Graph Algorithms (Dfs, Bfs, Dijkstras, Etc)

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Graphics And Rendering

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Gui Programming With Python

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Hadoop Architecture

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Hadoop Cluster Management

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Hadoop Input Output Operations

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Hashing

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Html Fonts, Tags And Elements

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Html Forms

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Html Forms Handling

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Html Images And Web Page Files

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Html Multimedia

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Html Text, Links, Table And Lists

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Html With Javascript And Jquery

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Inheritance And Polymorphism In Ruby

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Inheritance In C Plus Plus

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Inheritence

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Input Output

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Interfaces And Abstract Classes

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Internet And Web Technology

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Internetworking

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Introduction To C Plus Plus

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Introduction To C Sharp

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Introduction To Data Science

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Introduction To Data Structures

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Introduction To Database

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Introduction To Hadoop

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Introduction To Python

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Introduction To R Programming

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Introduction To Sql

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Invocation And Performance Navigation

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Ios And Security Device Manager

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Ip Routing

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Java Autoboxing

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Java Beans And Jdbc

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Java Serialization And Networking

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Java Server And Servlet

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Jdbc, Java Server Pages, And Mysql

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Linked Lists In Data Structures

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Linq (Language Integrated Query) In C Sharp

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Lists In Python

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Logical Database Design

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Machine Learning Algorithms

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Managing A Cisco Internetwork

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Managing Databases With Oracle

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Managing Multiuser Databases

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Mapreduce Programming Model

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Memory Allocation

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Methods In Ruby

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Microsoft Excel

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Microsoft Powerpoint

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Microsoft Windows

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Microsoft Word

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Miscellaneous

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Miscellaneous In C Sharp

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Miscellaneous In Java

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Miscellaneous On Data Structures

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Model Evaluation And Validation

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Modeling Data In The Organization

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Module In Python

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Modules And Mixins In Ruby

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Ms Access

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Ms Excel

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Ms Word

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Multi Threading In C Plus Plus

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Multithreading In C Sharp

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Natural Language Processing (Nlp)

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Network Address Translation

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Networking Basics

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Networking In C Sharp

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Networking Miscellaneous

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Nosql Databases With Hbase

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Object Oriented Concept

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Object Oriented Data Modeling

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Object Oriented Database

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Object Oriented Programming (Oop) Concepts In Ruby

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Object Oriented Programming In C Plus Plus

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Odbc, Ole Db, Ado, And Asp

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Operating System

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Operators

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Operators And Expressions

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Operators And Expressions In C Plus Plus

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Operators And Expressions In C Sharp

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Operators And Expressions In Php

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Operators In Ruby

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Overriding And Overloading

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Parsing, Benchmarking And Logging

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Performance Measures

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Php Filter

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Php Networking

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Physical Database Design

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Plus Plus Miscellaneous

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Pointer

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Pointers And References In C Plus Plus

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Polymorphism In C Plus Plus

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Power Point

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Precedence And Associativity

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Preprocessor

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Programming Basics

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Programming Packages

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Python Built In Functions

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Querying With Hive

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Queues In Data Structures

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Realtime Processing With Apache Spark

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Regular Expressions

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Regular Expressions In Java

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Regular Expressions In Python

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Regular Expressions In Ruby

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Ruby Gems

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Searching Algorithms

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Security

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Security In Hadoop

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Server Side And Client Side Scripting

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Session Management, Jsp And Api

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Sets In Python

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Sockets

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Sorting Algorithms

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Spanning Tree Protocol

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Sql For Database Construction

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Sql Server 2000

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Stacks In Data Structures

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Standard Libraries In Data Structures

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Standard Library Functions

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Standard Template Library (Stl) In C Plus Plus

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Statistical Analysis With R (Distributions, Graphics)

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Storage Class

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String Matching

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Strings

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Strings In Python

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Structure And Union

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Structures And Unions In C Plus Plus

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Subnetting

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Testing In Ruby (Rspec, Minitest, Etc.)

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The Database Development Process

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The Database Environment

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The Internet Database Environment

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The Relational Model And Normalization

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Threads

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Time Series Analysis

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Trees In Data Structures

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Tuples In Python

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Variable Names And Operators

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Variables And Constants In C Sharp

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Variables And Data Types In C Plus Plus

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Virtual Lans

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Web Scraping With Python

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Website Security

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Wide Area Networks

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Windows Forms Applications With C Sharp

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Wireless Technologies

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Workflow Automation With Oozie

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Working With Dates And Times In Ruby

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Xml And Ado.Net

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Latest Computer MCQs

Recently added questions from this subject.

What is the space complexity of the following dynamic programming implementation of the balanced partition problem? #include int balanced_partition(int *arr, int len) { int sm = 0, i, j; for(i = 0;i < len; i++) sm += arr[i]; if(sm % 2 != 0) return 0; int ans[sm/2 + 1][len + 1]; for(i = 0; i <= len; i++) ans[0][i] = 1; for(i = 1; i <= sm/2; i++) ans[i][0] = 0; for(i = 1; i <= sm/2; i++) { for(j = 1;j = arr[j - 1]) ans[i][j] = ans[i][j] || ans[i - arr[j - 1]][j - 1]; } } return ans[sm/2][len]; } int main() { int arr[] = {3, 4, 5, 6, 7, 1}, len = 6; int ans = balanced_partition(arr,len); if(ans == 0) printf("false"); else printf("true"); return 0; }

  1. A. O(sum)
  2. B. O(n)
  3. C. O(sum * n)
  4. D. O(sum + n)
Report Error

The following sequence is a fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, .... Which technique can be used to get the nth fibonacci term?

  1. A. Recursion
  2. B. Dynamic programming
  3. C. A single for loop
  4. D. Recursion, Dynamic Programming, For loops
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What is the output of the following program? #include int max_num(int a,int b) { if(a> b) return a; return b; } int maximum_subarray_sum(int *arr, int len) { int sum[len], idx; sum[0] = arr[0]; for(idx = 1; idx < len; idx++) sum[idx] = max_num(sum[idx - 1] + arr[idx], arr[idx]); int mx = sum[0]; for(idx = 0; idx mx) mx =sum[idx]; return mx; } int main() { int arr[] = {-20, 23, 10, 3, -10, 11, -5},len = 7; int ans = maximum_subarray_sum(arr, len); printf("%d",ans); return 0; }

  1. A. 27
  2. B. 37
  3. C. 36
  4. D. 26
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Consider the following code to find the nth fibonacci term using dynamic programming: 1. int fibo(int n) 2. int fibo_terms[100000] //arr to store the fibonacci numbers 3. fibo_terms[0] = 0 4. fibo_terms[1] = 1 5. 6. for i: 2 to n 7. fibo_terms[i] = fibo_terms[i - 1] + fibo_terms[i - 2] 8. 9. return fibo_terms[n] Which technique is used by line 7 of the above code?

  1. A. Greedy
  2. B. Recursion
  3. C. Memoization
  4. D. Overlapping subproblems
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If a problem can be solved by combining optimal solutions to non-overlapping problems, the strategy is called . . . . . . . .

  1. A. Dynamic programming
  2. B. Greedy
  3. C. Divide and conquer
  4. D. Recursion
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Consider the two matrices P and Q which are 10 x 20 and 20 x 30 matrices respectively. What is the number of multiplications required to multiply the two matrices?

  1. A. 10*20
  2. B. 20*30
  3. C. 10*30
  4. D. 10*20*30
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Complete the following dynamic programming implementation of the longest increasing subsequence problem: #include int longest_inc_sub(int *arr, int len) { int i, j, tmp_max; int LIS[len]; // array to store the lengths of the longest increasing subsequence LIS[0]=1; for(i = 1; i < len; i++) { tmp_max = 0; for(j = 0; j < i; j++) { if(arr[j] tmp_max) ___________; } } LIS[i] = tmp_max + 1; } int max = LIS[0]; for(i = 0; i max) max = LIS[i]; return max; } int main() { int arr[] = {10,22,9,33,21,50,41,60,80}, len = 9; int ans = longest_inc_sub(arr, len); printf("%d",ans); return 0; }

  1. A. tmp_max = LIS[j]
  2. B. LIS[i] = LIS[j]
  3. C. LIS[j] = tmp_max
  4. D. tmp_max = LIS[i]
Report Error

What is the output of the following naive method used to find the maximum sub-array sum? #include int main() { int arr[1000] = {-2, -5, 6, -2, 3, -1, 0,-5, 6}, len = 9; int cur_max, tmp_max, strt_idx, sub_arr_idx; cur_max = arr[0]; for(strt_idx = 0; strt_idx < len; strt_idx++) { tmp_max = 0; for(sub_arr_idx = strt_idx; sub_arr_idx cur_max) cur_max = tmp_max; } } printf("%d",cur_max); return 0; }

  1. A. 6
  2. B. 9
  3. C. 7
  4. D. 4
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What is the output of the following code? #include #include int count_bool_parenthesization(char *sym, char *op) { int str_len = strlen(sym); int True[str_len][str_len],False[str_len][str_len]; int row,col,length,l; for(row = 0, col = 0; row < str_len; row++,col++) { if(sym[row] == 'T') { True[row][col] = 1; False[row][col] = 0; } else { True[row][col] = 0; False[row][col] = 1; } } for(length = 1; length < str_len; length++) { for(row = 0, col = length; col < str_len; col++, row++) { True[row][col] = 0; False[row][col] = 0; for(l = 0; l < length; l++) { int pos = row + l; int t_row_pos = True[row][pos] + False[row][pos]; int t_pos_col = True[pos+1][col] + False[pos+1][col]; if(op[pos] == '|') { False[row][col] += False[row][pos] * False[pos+1][col]; True[row][col] += t_row_pos * t_pos_col - False[row][pos] * False[pos+1][col]; } if(op[pos] == '&') { True[row][col] += True[row][pos] * True[pos+1][col]; False[row][col] += t_row_pos * t_pos_col - True[row][pos] * True[pos+1][col]; } if(op[pos] == '^') { True[row][col] += True[row][pos] * False[pos+1][col] + False[row][pos] * True[pos + 1][col]; False[row][col] += True[row][pos] * True[pos+1][col] + False[row][pos] * False[pos+1][col]; } } } } return True[0][str_len-1]; } int main() { char sym[] = "TTTT"; char op[] = "|^^"; int ans = count_bool_parenthesization(sym,op); printf("%d",ans); return 0; }

  1. A. 1
  2. B. 2
  3. C. 3
  4. D. 4
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What is the value stored in max_val[5] after the following program is executed? #include #include int rod_cut(int *prices, int len) { int max_val[len + 1]; int i,j,tmp_price,tmp_idx; max_val[0] = 0; for(i = 1; i <= len; i++) { int tmp_max = INT_MIN; // minimum value an integer can hold for(j = 1; j tmp_max) tmp_max = tmp_price; } max_val[i] = tmp_max; } return max_val[len]; } int main() { int prices[]={2, 5, 6, 9, 9, 17, 17, 18, 20, 22},len_of_rod = 5; int ans = rod_cut(prices, len_of_rod); printf("%d",ans); return 0; }

  1. A. 12
  2. B. 27
  3. C. 10
  4. D. 17
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What is the space complexity of the following dynamic programming implementation of the edit distance problem where "m" and "n" are the lengths of the two strings? #include #include int get_min(int a, int b) { if(a < b) return a; return b; } int edit_distance(char *s1, char *s2) { int len1,len2,i,j,min; len1 = strlen(s1); len2 = strlen(s2); int arr[len1 + 1][len2 + 1]; for(i = 0;i <= len1; i++) arr[i][0] = i; for(i = 0; i <= len2; i++) arr[0][i] = i; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { min = get_min(arr[i-1][j],arr[i][j-1]) + 1; if(s1[i - 1] == s2[j - 1]) { if(arr[i-1][j-1] < min) min = arr[i-1][j-1]; } else { if(arr[i-1][j-1] + 1 < min) min = arr[i-1][j-1] + 1; } arr[i][j] = min; } } return arr[len1][len2]; } int main() { char s1[] = "abcd", s2[] = "defg"; int ans = edit_distance(s1, s2); printf("%d",ans); return 0; }

  1. A. O(1)
  2. B. O(m + n)
  3. C. O(mn)
  4. D. O(n)
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Given a one-dimensional array of integers, you have to find a sub-array with maximum sum. This is the maximum sub-array sum problem. Which of these methods can be used to solve the problem?

  1. A. Dynamic programming
  2. B. Two for loops (naive method)
  3. C. Divide and conquer
  4. D. Dynamic programming, naive method and Divide and conquer methods
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