| 1. | __________ is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions. |
| A. | Data Mining |
| B. | Data Warehousing |
| C. | Web Mining |
| D. | Text Mining |
| Answer» B. Data Warehousing | |
| 2. | The data Warehouse is __________. |
| A. | Read only |
| B. | Write only |
| C. | Read write only |
| D. | None |
| Answer» A. Read only | |
| 3. | Expansion for DSS in DW is _________. |
| A. | Decision Support system |
| B. | Decision Single System |
| C. | Data Storable System |
| D. | Data Support System |
| Answer» A. Decision Support system | |
| 4. | The important aspect of the data warehouse environment is that data found within the data warehouse is__________. |
| A. | subject-oriented |
| B. | time-variant |
| C. | integrate |
| D. | D All of the above |
| Answer» D. D All of the above | |
| 5. | The time horizon in Data warehouse is usually__________. |
| A. | 1-2 years |
| B. | 3-4years |
| C. | 5-6 years |
| D. | 5-10 years |
| Answer» D. 5-10 years | |
| 6. | The data is stored, retrieved & updated in___________. |
| A. | OLAP |
| B. | OLTP |
| C. | SMTP |
| D. | FTP |
| Answer» B. OLTP | |
| 7. | __________describes the data contained in the data warehouse. |
| A. | Relational data |
| B. | Operational data |
| C. | Metadata |
| D. | Informational data |
| Answer» C. Metadata | |
| 8. | __________predicts future trends & behaviors, allowing business managers to make proactive, knowledge-driven decisions. |
| A. | Data warehouse |
| B. | Data mining |
| C. | Data marts |
| D. | Metadata |
| Answer» B. Data mining | |
| 9. | _________is the heart of the warehouse. |
| A. | Data mining database servers |
| B. | Data warehouse database servers |
| C. | Data mart database servers |
| D. | Relational data base servers |
| Answer» B. Data warehouse database servers | |
| 10. | _________is the specialized data warehouse database. |
| A. | Oracle |
| B. | DBZ |
| C. | Informix |
| D. | Redbrick |
| Answer» D. Redbrick | |
| 11. | ___________defines the structure of the data held in operational databases and used by operational applications. |
| A. | User-level metadata |
| B. | Data warehouse metadata |
| C. | Operational metadata |
| D. | Data mining metadata |
| Answer» C. Operational metadata | |
| 12. | _________is held in the catalog of the warehouse database system. |
| A. | Application level metadata |
| B. | Algorithmic level metadata |
| C. | Departmental level metadata |
| D. | Core warehouse metadata |
| Answer» B. Algorithmic level metadata | |
| 13. | __________maps the core warehouse metadata to business concepts, familiar and useful to end users. |
| A. | Application level metadata |
| B. | User level metadata |
| C. | Enduser level metadata |
| D. | Core level metadata |
| Answer» A. Application level metadata | |
| 14. | ____________ consists of formal definitions, such as a COBOL layout or a database schema. |
| A. | Classical metadata |
| B. | Transformation metadata |
| C. | Historical metadata |
| D. | Structural metadata |
| Answer» A. Classical metadata | |
| 15. | ________consists of information in the enterprise that is not in classical form. |
| A. | Mushy metadata |
| B. | Differential metadata |
| C. | Data warehouse |
| D. | Data mining |
| Answer» A. Mushy metadata | |
| 16. | ________ Databases are owned by particular departments or business groups. |
| A. | Informational |
| B. | Operational |
| C. | Both informational and operational |
| D. | Flat |
| Answer» B. Operational | |
| 17. | The star schema is composed of __________ fact table. |
| A. | one |
| B. | Two |
| C. | Three |
| D. | four |
| Answer» A. one | |
| 18. | The time horizon in operational environment is ________. |
| A. | 30-60 days |
| B. | 60-90 days |
| C. | 90-120 days |
| D. | 120-150 days |
| Answer» B. 60-90 days | |
| 19. | The key used in operational environment may not have an element of _________. |
| A. | Time |
| B. | Cost |
| C. | Frequency |
| D. | Quality |
| Answer» A. Time | |
| 20. | Data can be updated in ______ environment. |
| A. | Data warehouse |
| B. | Data mining |
| C. | Operational |
| D. | Informational |
| Answer» C. Operational | |
| 21. | Record cannot be updated in__________. |
| A. | OLTP |
| B. | Files |
| C. | RDBMS |
| D. | data warehouse |
| Answer» D. data warehouse | |
| 22. | The source of all data warehouse data is the__________. |
| A. | Operational environment |
| B. | Informal environment |
| C. | Formal environment |
| D. | Technology environment |
| Answer» A. Operational environment | |
| 23. | Data warehouse contains ______data that is never found in the operational environment. |
| A. | Normalized |
| B. | Informational |
| C. | Summary |
| D. | Denormalized |
| Answer» C. Summary | |
| 24. | The modern CASE tools belong to_________category. |
| A. | Analysis |
| B. | Development |
| C. | Coding |
| D. | Delivery |
| Answer» A. Analysis | |
| 26. | Detail data in single fact table is otherwise known as__________. |
| A. | Monoatomic data |
| B. | Diatomic data |
| C. | Atomic data |
| D. | Multiatomic data |
| Answer» C. Atomic data | |
| 27. | ______test is used in an online transactional processing environment. |
| A. | MEGA |
| B. | MICRO |
| C. | MACRO |
| D. | ACID |
| Answer» D. ACID | |
| 28. | ______is a good alternative to the star schema. |
| A. | Star schema |
| B. | Snowflake schema |
| C. | Fact constellation |
| D. | Star-snowflake schema |
| Answer» C. Fact constellation | |
| 29. | The biggest drawback of the level indicator in the classic star-schema is thatit limits_________. |
| A. | Quantify |
| B. | Qualify |
| C. | Flexibility |
| D. | Ability |
| Answer» C. Flexibility | |
| 30. | A data warehouse is__________. |
| A. | Updated by end users |
| B. | Contains numerous naming conventions and formats |
| C. | Organized around important subject areas |
| D. | Contains only current data |
| Answer» C. Organized around important subject areas | |
| 31. | An operational system is________. |
| A. | Used to run the business in real time and is based on historical data |
| B. | Used to run the business in real time and is based on current data |
| C. | Used to support decision making and is based on current data |
| D. | Used to support decision making and is based on historical data |
| Answer» B. Used to run the business in real time and is based on current data | |
| 32. | The generic two-level data warehouse architecture includes________. |
| A. | At least one data mart |
| B. | Data that can extracted from numerous internal and external sources |
| C. | Near real-time updates |
| D. | Far real-time updates |
| Answer» C. Near real-time updates | |
| 33. | The active data warehouse architecture includes _________. |
| A. | At least one data mart |
| B. | Data that can extracted from numerous internal and external sources |
| C. | Near real-time updates |
| D. | All of the above |
| Answer» D. All of the above | |
| 34. | Reconciled data is_________. |
| A. | Data stored in the various operational systems throughout the organization |
| B. | Current data intended to be the single source for all decision support systems |
| C. | Data stored in one operational system in the organization |
| D. | Data that has been selected and formatted for end-user support applications |
| Answer» B. Current data intended to be the single source for all decision support systems | |
| 35. | Transient data is___________. |
| A. | Data in which changes to existing records cause the previous version of the records to be eliminated |
| B. | Data in which changes to existing records do not cause the previous version of the records to be eliminated |
| C. | Data that are never altered or deleted once they have been adde |
| D. | D Data that are never deleted once they have been added |
| Answer» A. Data in which changes to existing records cause the previous version of the records to be eliminated | |
| 36. | The extract process is __________. |
| A. | Capturing all of the data contained in various operational systems |
| B. | Capturing a subset of the data contained in various operational systems |
| C. | Capturing all of the data contained in various decision support systems |
| D. | Capturing a subset of the data contained in various decision supportsystems |
| Answer» B. Capturing a subset of the data contained in various operational systems | |
| 37. | Data scrubbing is___________. |
| A. | A process to reject data from the data warehouse and to create the necessary indexes |
| B. | A process to load the data in the data warehouse and to create the necessary indexes |
| C. | A process to upgrade the quality of data after it is moved into a data warehouse |
| D. | A process to upgrade the quality of data before it is moved into a data warehouse |
| Answer» D. A process to upgrade the quality of data before it is moved into a data warehouse | |
| 38. | The load and index is_______. |
| A. | A process to reject data from the data warehouse and to create the necessary indexes |
| B. | A process to load the data in the data warehouse and to create the necessary indexes |
| C. | A process to upgrade the quality of data after it is moved into a data warehouse |
| D. | A process to upgrade the quality of data before it is moved into a data warehouse |
| Answer» B. A process to load the data in the data warehouse and to create the necessary indexes | |
| 39. | Data transformation includes___________. |
| A. | A process to change data from a detailed level to a summary level |
| B. | A process to change data from a summary level to a detailed level |
| C. | Joining data from one source into various sources of data |
| D. | Separating data from one source into various sources of data |
| Answer» A. A process to change data from a detailed level to a summary level | |
| 40. | ______ is called a multifield transformation. |
| A. | Converting data from one field into multiple fields |
| B. | Converting data from fields into field |
| C. | Converting data from double fields into multiple fields |
| D. | Converting data from one field to one field |
| Answer» A. Converting data from one field into multiple fields | |
| 41. | The type of relationship in star schema is_________. |
| A. | Many-to-many |
| B. | One-to-one |
| C. | One-to-many |
| D. | Many-to-one |
| Answer» C. One-to-many | |
| 42. | Fact tables are_______. |
| A. | Completely demoralized |
| B. | Partially demoralized |
| C. | Completely normalize |
| D. | D Partially normalized |
| Answer» C. Completely normalize | |
| 43. | ____________is the goal of data mining. |
| A. | To explain some observed event or condition |
| B. | To confirm that data exists |
| C. | To analyze data for expected relationships |
| D. | To create a new data warehouse |
| Answer» A. To explain some observed event or condition | |
| 44. | Business Intelligence and data warehousing is used for________. |
| A. | Forecasting |
| B. | Data Mining |
| C. | Analysis of large volumes of product sales data |
| D. | All of the above |
| Answer» D. All of the above | |
| 45. | The data administration subsystem helps you perform all of the following, except_________. |
| A. | Backups and recovery |
| B. | Query optimization |
| C. | Security management |
| D. | Create, change, and delete information |
| Answer» D. Create, change, and delete information | |
| 46. | The most common source of change data in refreshing a data warehouse is__________. |
| A. | Query able change data |
| B. | Cooperative change data |
| C. | Logged change data |
| D. | Snapshot change data |
| Answer» A. Query able change data | |
| 47. | ___________are responsible for running queries and reports against data warehouse tables. |
| A. | Hardware |
| B. | Software |
| C. | End users |
| D. | Middle ware |
| Answer» C. End users | |
| 48. | Query tool is meant for_________. |
| A. | Data acquisition |
| B. | Information delivery |
| C. | Information exchange |
| D. | Communication |
| Answer» A. Data acquisition | |
| 49. | Classification rules are extracted from____________. |
| A. | Root node |
| B. | Decision tree |
| C. | Siblings |
| D. | Branches |
| Answer» B. Decision tree | |
| 51. | ___________is a method of incremental conceptual clustering. |
| A. | CORBA |
| B. | OLAP |
| C. | COBWEB |
| D. | STING |
| Answer» C. COBWEB | |
| 52. | Effect of one attribute value on a given class is independent of values of other attribute is called____________. |
| A. | Value independence |
| B. | Class conditional independence |
| C. | Conditional independence |
| D. | Unconditional independence |
| Answer» A. Value independence | |
| 53. | The main organizational justification for implementing a data warehouse is to provide__________. |
| A. | Cheaper ways of handling transportation |
| B. | Decision support |
| C. | Storing large volume of data |
| D. | Access to data |
| Answer» C. Storing large volume of data | |
| 54. | Multidimensional database is otherwise known as___________. |
| A. | RDBMS |
| B. | DBMS |
| C. | EXTENDED RDBMS |
| D. | EXTENDED DBMS |
| Answer» B. DBMS | |
| 55. | Data warehouse architecture is based on___________. |
| A. | DBMS |
| B. | RDBMS |
| C. | Sybase |
| D. | SQL Server |
| Answer» B. RDBMS | |
| 56. | Source data from the warehouse comes from__________. |
| A. | ODS |
| B. | TDS |
| C. | MDDB |
| D. | ORDBMS |
| Answer» A. ODS | |
| 57. | ___________is a data transformation process. |
| A. | Comparison |
| B. | Projection |
| C. | Selection |
| D. | Filtering |
| Answer» D. Filtering | |
| 58. | The technology area associated with CRM is__________. |
| A. | Specialization |
| B. | Generalization |
| C. | Personalization |
| D. | Summarization |
| Answer» C. Personalization | |
| 59. | SMP stands for________. |
| A. | Symmetric Multiprocessor |
| B. | Symmetric Multiprogramming |
| C. | Symmetric Metaprogramming |
| D. | Symmetric Microprogramming |
| Answer» A. Symmetric Multiprocessor | |
| 60. | _____________are designed to overcome any limitations placed on the warehouse by the nature of the relational data model. |
| A. | Operational database |
| B. | Relational database |
| C. | Multidimensional database |
| D. | Data repository |
| Answer» C. Multidimensional database | |
| 61. | __________are designed to overcome any limitations placed on the warehouse by the nature of the relational data model. |
| A. | Operational database |
| B. | Relational database |
| C. | Multidimensional database |
| D. | Data repository |
| Answer» C. Multidimensional database | |
| 62. | MDDB stands for _______________. |
| A. | Multiple data doubling |
| B. | Multidimensional databases |
| C. | Multiple double dimension |
| D. | Multi-dimension doubling |
| Answer» B. Multidimensional databases | |
| 63. | ____________is data about data. |
| A. | Metadata |
| B. | Microdata |
| C. | Minidata |
| D. | Multidata |
| Answer» A. Metadata | |
| 64. | _____________is an important functional component of the metadata. |
| A. | Digital directory |
| B. | Repository |
| C. | Information directory |
| D. | Data dictionary |
| Answer» C. Information directory | |
| 65. | EIS stands for____________. |
| A. | Extended interface system |
| B. | Executive interface system |
| C. | Executive information system |
| D. | Extendable information system |
| Answer» C. Executive information system | |
| 66. | ____________is data collected from natural systems. |
| A. | MRI scan |
| B. | ODS data |
| C. | Statistical data |
| D. | Historical data |
| Answer» A. MRI scan | |
| 67. | __________is an example of application development environments. |
| A. | Visual Basic |
| B. | Oracle |
| C. | Sybase |
| D. | SQL Server |
| Answer» A. Visual Basic | |
| 68. | The term that is not associated with data cleaning process is____________. |
| A. | Domain consistency |
| B. | Deduplication |
| C. | Disambiguation |
| D. | Segmentation |
| Answer» D. Segmentation | |
| 69. | __________are some popular OLAP tools. |
| A. | Metacube, Informix |
| B. | Oracle Express, Essbase |
| C. | HOLAP |
| D. | MOLAP |
| Answer» A. Metacube, Informix | |
| 70. | Capability of data mining is to build __________models. |
| A. | Retrospective |
| B. | Interrogative |
| C. | Predictive |
| D. | Imperative |
| Answer» C. Predictive | |
| 71. | ________is a process of determining the preference of customer’s majority. |
| A. | Association |
| B. | Preferencing |
| C. | Segmentation |
| D. | Classification |
| Answer» B. Preferencing | |
| 72. | Strategic value of data mining is_______. |
| A. | Cost-sensitive |
| B. | Work-sensitive |
| C. | Time-sensitive |
| D. | Technical-sensitive |
| Answer» C. Time-sensitive | |
| 73. | ___________proposed the approach for data integration issues. |
| A. | Ralph Campbell |
| B. | Ralph Kimball |
| C. | John Raphlin |
| D. | James Gosling |
| Answer» B. Ralph Kimball | |
| 74. | The terms equality and roll up are associated with__________. |
| A. | OLAP |
| B. | Visualization |
| C. | Data mart |
| D. | Decision tree |
| Answer» C. Data mart | |
| 76. | _________is a metadata repository. |
| A. | Prism solution directory manager |
| B. | CORBA |
| C. | STUNT |
| D. | COBWEB |
| Answer» A. Prism solution directory manager | |
| 77. | _________is an expensive process in building an expert system. |
| A. | Analysis |
| B. | Study |
| C. | Design |
| D. | Information collection |
| Answer» D. Information collection | |
| 78. | The full form of KDD is__________. |
| A. | Knowledge database |
| B. | Knowledge discovery in database |
| C. | Knowledge data house |
| D. | Knowledge data definition |
| Answer» B. Knowledge discovery in database | |
| 79. | The first International conference on KDD was held in the year_________. |
| A. | 1996 |
| B. | 1997 |
| C. | 1995 |
| D. | 1994 |
| Answer» C. 1995 | |
| 80. | Removing duplicate records is a process called____________. |
| A. | Recovery |
| B. | Data cleaning |
| C. | Data cleansing |
| D. | Data pruning |
| Answer» B. Data cleaning | |
| 81. | __________contains information that gives users an easy-to-understand perspective of the information stored in the data warehouse. |
| A. | Business metadata |
| B. | Technical metadata |
| C. | Operational metadata |
| D. | Financial metadata |
| Answer» A. Business metadata | |
| 82. | _________helps to integrate, maintain and view the contents of the data warehousing system. |
| A. | Business directory |
| B. | Information directory |
| C. | Data dictionary |
| D. | Database |
| Answer» B. Information directory | |
| 83. | Discovery of cross-sales opportunities is called _________. |
| A. | Segmentation |
| B. | Visualization |
| C. | Correction |
| D. | Association |
| Answer» D. Association | |
| 84. | Data marts that incorporate data mining tools to extract sets of data are called_________. |
| A. | Independent data mart |
| B. | Dependent data marts |
| C. | Intra-entry data mart |
| D. | Inter-entry data mart |
| Answer» B. Dependent data marts | |
| 85. | __________can generate programs itself, enabling it to carry out new tasks. |
| A. | Automated system |
| B. | Decision making system |
| C. | Self-learning system |
| D. | Productivity system |
| Answer» D. Productivity system | |
| 86. | The power of self-learning system lies in___________. |
| A. | Cost |
| B. | Speed |
| C. | Accuracy |
| D. | Simplicity |
| Answer» C. Accuracy | |
| 87. | Building the informational database is done with the help of ___________. |
| A. | Transformation or propagation tools |
| B. | Transformation tools only |
| C. | Propagation tools only |
| D. | Extraction tools |
| Answer» A. Transformation or propagation tools | |
| 88. | How many components are there in a data warehouse? |
| A. | Two |
| B. | Three |
| C. | Four |
| D. | Five |
| Answer» D. Five | |
| 89. | Which of the following is not a component of a data warehouse? |
| A. | Metadata |
| B. | Current detail data |
| C. | Lightly summarized data |
| D. | Component Key |
| Answer» D. Component Key | |
| 90. | ___________is data that is distilled from the low level of detail found at the current detailed leve. |
| A. | Highly summarized data |
| B. | Lightly summarized data |
| C. | Metadata |
| D. | Older detail data |
| Answer» B. Lightly summarized data | |
| 91. | Highly summarized data is __________. |
| A. | Compact and easily accessible |
| B. | Compact and expensive |
| C. | Compact and hardly accessible |
| D. | Compact |
| Answer» A. Compact and easily accessible | |
| 92. | A directory to help the DSS analyst locate the contents of the data warehouse isseen in __________. |
| A. | Current detail data |
| B. | Lightly summarized data |
| C. | Metadata |
| D. | Older detail data |
| Answer» C. Metadata | |
| 93. | Metadata contains at least__________. |
| A. | The structure of the data |
| B. | The algorithms used for summarization |
| C. | The mapping from the operational environment to the data warehouse |
| D. | All of the above |
| Answer» D. All of the above | |
| 94. | Which of the following is not a old detail storage medium? |
| A. | Phot Optical Storage |
| B. | RAID |
| C. | Microfinche |
| D. | Pen drive |
| Answer» D. Pen drive | |
| 95. | The data from the operational environment enter_____________of data warehouse. |
| A. | Current detail data |
| B. | Older detail data |
| C. | Lightly summarized data |
| D. | Highly summarized data |
| Answer» A. Current detail data | |
| 96. | The data in current detail level resides till__________event occurs. |
| A. | Purge |
| B. | Summarization |
| C. | Archieve |
| D. | D All of the above |
| Answer» D. D All of the above | |
| 97. | The dimension tables describe the __________. |
| A. | Entities |
| B. | Facts |
| C. | Keys |
| D. | Units of measures |
| Answer» B. Facts | |
| 98. | The granularity of the fact is the ___________ of detail at which it is recorded. |
| A. | Transformation |
| B. | Summarization |
| C. | Level |
| D. | Tr |
| Answer» A. Transformation | |
| 99. | Which of the following is not a primary grain in analytical modeling. |
| A. | Transaction |
| B. | Periodic snapshot |
| C. | Accumulating snapshot |
| D. | All of the above |
| Answer» B. Periodic snapshot | |
| 101. | ________of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity. | |
| A. | Additively | |
| B. | Granularity | |
| C. | Functional dependency | |
| D. | Dimensionality | |
| Answer» C. Functional dependency | ||
| 102. | A fact is said to be fully additive if_________. |
| A. | It is additive over every dimension of its dimensionality |
| B. | Additive over at least one but not all of the dimensions |
| C. | Not additive over any dimension |
| D. | None of the above |
| Answer» A. It is additive over every dimension of its dimensionality | |
| 103. | A fact is said to be partially additive if_______. |
| A. | It is additive over every dimension of its dimensionality |
| B. | Additive over at least one but not all of the dimensions |
| C. | Not additive over any dimension |
| D. | None of the above |
| Answer» B. Additive over at least one but not all of the dimensions | |
| 104. | A fact is said to be non-additive if_______. |
| A. | It is additive over every dimension of its dimensionality |
| B. | Additive over at least one but not all of the dimensions |
| C. | Not additive over any dimension |
| D. | None of the above |
| Answer» C. Not additive over any dimension | |
| 105. | Non-additive measures can often combined with additive measures to create new_________. |
| A. | Additive measures |
| B. | Non-additive measures |
| C. | Partially additive |
| D. | All of the above |
| Answer» A. Additive measures | |
| 106. | A fact representing cumulative sales units over a day at a store for a product is a_________. |
| A. | Additive fact |
| B. | Fully additive fact |
| C. | Partially additive fact |
| D. | Non-additive fact |
| Answer» B. Fully additive fact | |
| 107. | Which of the following is the other name of Data mining? |
| A. | Exploratory data analysis |
| B. | Data driven discovery |
| C. | Deductive learning |
| D. | All of the above |
| Answer» D. All of the above | |
| 108. | Which of the following is a predictive model? |
| A. | Clustering |
| B. | Regression |
| C. | Summarization |
| D. | Association rules |
| Answer» B. Regression | |
| 109. | Which of the following is a descriptive model? |
| A. | Classification |
| B. | Regression |
| C. | Sequence discovery |
| D. | Association rules |
| Answer» C. Sequence discovery | |
| 110. | A_________model identifies patterns or relationships. |
| A. | Descriptive |
| B. | Predictive |
| C. | Regression |
| D. | Time series analysis |
| Answer» A. Descriptive | |
| 111. | A predictive model makes use of______. |
| A. | Current data. |
| B. | Historical data. |
| C. | Both current and historical data. |
| D. | Assumptions |
| Answer» B. Historical data. | |
| 112. | ______ maps data into predefined groups. |
| A. | Regression |
| B. | Time series analysis |
| C. | Prediction |
| D. | Classification |
| Answer» D. Classification | |
| 113. | _____ is used to map a data item to a real valued prediction variable. |
| A. | Regression |
| B. | Time series analysis |
| C. | Prediction |
| D. | Classification |
| Answer» B. Time series analysis | |
| 114. | In _____ , the value of an attribute is examined as it varies over time. |
| A. | Regression |
| B. | Time series analysis |
| C. | Sequence discovery |
| D. | Prediction |
| Answer» B. Time series analysis | |
| 115. | In ______ the groups are not predefined. |
| A. | Association rules |
| B. | Summarization |
| C. | Clustering |
| D. | Prediction |
| Answer» C. Clustering | |
| 116. | Link Analysis is otherwise called as ____. |
| A. | Affinity analysis |
| B. | Association rules |
| C. | Both A & B |
| D. | Prediction |
| Answer» C. Both A & B | |
| 117. | ______ is a the input to KDD. |
| A. | Data |
| B. | Information |
| C. | Query |
| D. | Process |
| Answer» A. Data | |
| 118. | The output of KDD is ______. |
| A. | Data |
| B. | Information |
| C. | Query |
| D. | Useful information |
| Answer» D. Useful information | |
| 119. | The KDD process consists of ____steps. |
| A. | Three |
| B. | Four |
| C. | Five |
| D. | Six |
| Answer» C. Five | |
| 120. | Treating incorrect or missing data is called as________. |
| A. | Selection |
| B. | Preprocessing |
| C. | Transformation |
| D. | Interpretation |
| Answer» B. Preprocessing | |
| 121. | Converting data from different sources into a common format for processing is called as____ . |
| A. | Selection |
| B. | Preprocessing |
| C. | Transformation |
| D. | Interpretation |
| Answer» C. Transformation | |
| 122. | Various visualization techniques are used in_________step of KDD. |
| A. | Selection |
| B. | Transformation |
| C. | Data mining |
| D. | Interpretation |
| Answer» D. Interpretation | |
| 123. | Extreme values that occur infrequently are called as___________. |
| A. | Outliers |
| B. | Rare values |
| C. | Dimensionality reduction |
| D. | All of the above |
| Answer» A. Outliers | |
| 124. | Box plot and scatter diagram techniques are_________. |
| A. | Graphical |
| B. | Geometri |
| C. | C Icon-base |
| D. | D Pixel-based |
| Answer» B. Geometri | |
| 126. | Describing some characteristics of a set of data by a general model is viewed as___________. | |
| A. | Induction. | |
| B. | Compression | |
| C. | Approximation | |
| D. | Summarization | |
| Answer» B. Compression | ||
| 127. | ______ helps to uncover hidden information about the data. |
| A. | Induction |
| B. | Compression |
| C. | Approximation |
| D. | Summarization |
| Answer» C. Approximation | |
| 128. | ______ are needed to identify training data and desired results. |
| A. | Programmers |
| B. | Designers |
| C. | Users |
| D. | Administrators |
| Answer» C. Users | |
| 129. | Over fitting occurs when a model_________. |
| A. | Does fit in future states |
| B. | Does not fit in future states |
| C. | Does fit in current state |
| D. | Does not fit in current state |
| Answer» B. Does not fit in future states | |
| 130. | The problem of dimensionality curse involves___________. |
| A. | The use of some attributes may interfere with the correct completion of a data mining task. |
| B. | The use of some attributes may simply increase the overall complexity. |
| C. | Some may decrease the efficiency of the algorithm. |
| D. | All of the above |
| Answer» D. All of the above | |
| 131. | Incorrect or invalid data is known as _______. |
| A. | Changing data |
| B. | Noisy data |
| C. | Outliers |
| D. | Missing data |
| Answer» B. Noisy data | |
| 132. | ROI is an acronym of _______. |
| A. | Return on Investment |
| B. | Return on Information |
| C. | Repetition of Information |
| D. | Runtime of Instruction |
| Answer» A. Return on Investment | |
| 133. | The ______of data could result in the disclosure of information that is deemed to be confidential. |
| A. | Authorized use |
| B. | Unauthorized use |
| C. | Authenticated use |
| D. | Unauthenticated use |
| Answer» B. Unauthorized use | |
| 134. | _________data are noisy and have many missing attribute values. |
| A. | Preprocessed |
| B. | Cleaned |
| C. | Real-worl |
| D. | D Tr |
| Answer» D. D Tr | |
| 135. | The rise of DBMS occurred in early _______. |
| A. | 1950’s |
| B. | 1960’s |
| C. | 1970’s |
| D. | 1980’s |
| Answer» C. 1970’s | |
| 136. | SQL stand for_________. |
| A. | Standard Query Language |
| B. | Structured Query Language |
| C. | Standard Quick List. |
| D. | Structured Query list |
| Answer» B. Structured Query Language | |
| 137. | Which of the following is not a data mining metric? |
| A. | Space complexity |
| B. | Time complexity |
| C. | ROI |
| D. | All of the above |
| Answer» D. All of the above | |
| 138. | Reducing the number of attributes to solve the high dimensionality problem is called as_____________. |
| A. | Dimensionality curse |
| B. | Dimensionality reduction |
| C. | Cleaning |
| D. | Over fitting |
| Answer» B. Dimensionality reduction | |
| 139. | Data that are not of interest to the data mining task is called as _____. |
| A. | Missing data |
| B. | Changing data |
| C. | Irrelevant data |
| D. | Noisy data |
| Answer» C. Irrelevant data | |
| 140. | _________are effective tools to attack the scalability problem. |
| A. | Sampling |
| B. | Parallelization |
| C. | Both A & B |
| D. | None of the above |
| Answer» C. Both A & B | |
| 141. | Market-basket problem was formulated by____________. |
| A. | Agrawal et al |
| B. | Steve et al |
| C. | Toda et al |
| D. | Simon et al |
| Answer» A. Agrawal et al | |
| 142. | Data mining helps in________. |
| A. | Inventory managemen |
| B. | Sales promotion strategies |
| C. | Marketing strategies |
| D. | All of the above |
| Answer» D. All of the above | |
| 143. | The proportion of transaction supporting X in T is called_____________. |
| A. | Confidence |
| B. | Support |
| C. | Support count |
| D. | All of the above |
| Answer» B. Support | |
| 144. | The absolute number of transactions supporting X in T is called _______. |
| A. | Confidence |
| B. | Support |
| C. | Support count |
| D. | None of the above |
| Answer» C. Support count | |
| 145. | The value that says that transactions in D that support X also support Y is called__________. |
| A. | Confidence |
| B. | Support |
| C. | Support count |
| D. | None of the above |
| Answer» A. Confidence | |
| 146. | If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the support of bread and jam is_________. |
| A. | 2% |
| B. | 20% |
| C. | 3% |
| D. | 30% |
| Answer» A. 2% | |
| 147. | 7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the confidence of buying bread with jam is____________. |
| A. | 33.33% |
| B. | 66.66% |
| C. | 45% |
| D. | 50% |
| Answer» D. 50% | |
| 148. | The left hand side of an association rule is called________. |
| A. | Consequent |
| B. | Onset |
| C. | Antecedent |
| D. | Precedent |
| Answer» C. Antecedent | |
| 149. | The right hand side of an association rule is called__________. |
| A. | Consequent |
| B. | Onset |
| C. | Antecedent |
| D. | Precedent |
| Answer» A. Consequent | |
| 151. | All set of items whose support is greater than the user-specified minimum support are called as_____________ |
| A. | Border set |
| B. | Frequent set |
| C. | Maximal frequent set |
| D. | Lattice |
| Answer» B. Frequent set | |
| 152. | If a set is a frequent set and no superset of this set is a frequent set, then it is called____________ |
| A. | Maximal frequent set |
| B. | Border set |
| C. | Lattice |
| D. | Infrequent sets |
| Answer» A. Maximal frequent set | |
| 153. | Any subset of a frequent set is a frequent set. This is_________ |
| A. | Upward closure property |
| B. | Downward closure property |
| C. | Maximal frequent set |
| D. | Border set |
| Answer» B. Downward closure property | |
| 154. | Any superset of an infrequent set is an infrequent set. This is ___________ |
| A. | Maximal frequent set |
| B. | Border set |
| C. | Upward closure property |
| D. | Downward closure property |
| Answer» C. Upward closure property | |
| 155. | If an itemset is not a frequent set and no superset of this is a frequent set, then it is |
| A. | Maximal frequent set |
| B. | Border set |
| C. | Upward closure property |
| D. | Downward closure property |
| Answer» B. Border set | |
| 156. | A priori algorithm is otherwise called as_________ |
| A. | Width-wise algorithm |
| B. | Level-wise algorithm |
| C. | Pincer-search algorithm |
| D. | FP growth algorithm |
| Answer» B. Level-wise algorithm | |
| 157. | The A Priori algorithm is a____________ |
| A. | Top-down search |
| B. | Breadth first search |
| C. | Depth first search |
| D. | Bottom-up search |
| Answer» D. Bottom-up search | |
| 158. | The first phase of A Priori algorithm is___________ |
| A. | Candidate generation |
| B. | Itemset generation |
| C. | Pruning |
| D. | Partitioning |
| Answer» A. Candidate generation | |
| 159. | The second phase of A Priori algorithm is____________ |
| A. | Candidate generation |
| B. | Itemset generation |
| C. | Pruning |
| D. | Partitioning |
| Answer» C. Pruning | |
| 160. | The step eliminates the extensions of (k-1)-itemsets which are not found to be frequent, from being considered for counting support. |
| A. | Candidate generation |
| B. | Pruning |
| C. | Partitioning |
| D. | Itemset eliminations |
| Answer» B. Pruning | |
| 161. | The a priori frequent itemset discovery algorithm moves in the lattice |
| A. | Upward |
| B. | Downward |
| C. | Breadthwise |
| D. | Both upward and downward |
| Answer» A. Upward | |
| 162. | After the pruning of a priori algorithm,__________will remain |
| A. | Only candidate set |
| B. | No candidate set |
| C. | Only border set |
| D. | No border set |
| Answer» B. No candidate set | |
| 163. | The number of iterations in a priori |
| A. | Increases with the size of the maximum frequent set |
| B. | Decreases with increase in size of the maximum frequent set |
| C. | Increases with the size of the data |
| D. | Decreases with the increase in size of the data |
| Answer» A. Increases with the size of the maximum frequent set | |
| 164. | MFCS is the acronym of____________ |
| A. | Maximum Frequency Control Set |
| B. | Minimal Frequency Control Set |
| C. | Maximal Frequent Candidate Set |
| D. | Minimal Frequent Candidate Set |
| Answer» C. Maximal Frequent Candidate Set | |
| 165. | Dynamic Itemset Counting Algorithm was proposed by |
| A. | Bin et al |
| B. | Argawal et at |
| C. | Toda et al |
| D. | Simon et at |
| Answer» A. Bin et al | |
| 166. | Itemsets in the category of structures have a counter and the stop number with them |
| A. | Dashed |
| B. | Circle |
| C. | Box |
| D. | Solid |
| Answer» A. Dashed | |
| 167. | The itemsets in the_________category structures are not subjected to any counting |
| A. | Dashes |
| B. | Box |
| C. | Soli |
| D. | D Circle |
| Answer» C. Soli | |
| 168. | Certain itemsets in the dashed circle whose support count reach support value during an iteration move into the______________ |
| A. | Dashed box |
| B. | Solid circle |
| C. | Solid box |
| D. | None of the above |
| Answer» A. Dashed box | |
| 169. | Certain itemsets enter afresh into the system and get into the , which are essentially the supersets of the itemsets that move from the dashed circle to the dashed box |
| A. | Dashed box |
| B. | Solid circle |
| C. | Solid box |
| D. | Dashed circle |
| Answer» D. Dashed circle | |
| 170. | The item sets that have completed on full pass move from dashed circle to________ |
| A. | Dashed box |
| B. | Solid circle |
| C. | Solid box |
| D. | None of the above |
| Answer» B. Solid circle | |
| 171. | The FP-growth algorithm has phases |
| A. | One |
| B. | Two |
| C. | Three |
| D. | Four |
| Answer» B. Two | |
| 172. | A frequent pattern tree is a tree structure consisting of ________ |
| A. | An item-prefix-tree |
| B. | A frequent-item-header table |
| C. | A frequent-item-node |
| D. | Both A & B |
| Answer» D. Both A & B | |
| 173. | The non-root node of item-prefix-tree consists of fields |
| A. | Two |
| B. | Three |
| C. | Four |
| D. | Five |
| Answer» B. Three | |
| 174. | The frequent-item-header-table consists of fields |
| A. | Only one. |
| B. | Two. |
| C. | Three. |
| D. | Four |
| Answer» B. Two. | |
| 176. | The transformed prefix paths of a node ‘a’ form a truncated database of pattern which cooccur with a is called________ | |
| A. | Suffix path | |
| B. | FP-tree | |
| C. | Conditional pattern base | |
| D. | Prefix path | |
| Answer» C. Conditional pattern base | ||
| 177. | The goal of________is to discover both the dense and sparse regions of a data set |
| A. | Association rule |
| B. | Classification |
| C. | Clustering |
| D. | Genetic Algorithm |
| Answer» C. Clustering | |
| 178. | Which of the following is a clustering algorithm? |
| A. | A priori |
| B. | CLARA |
| C. | Pincer-Search |
| D. | FP-growth |
| Answer» B. CLARA | |
| 179. | clustering technique start with as many clusters as there are records, with each cluster having only one record |
| A. | Agglomerative |
| B. | Divisive |
| C. | Partition |
| D. | Numeric |
| Answer» A. Agglomerative | |
| 180. | clustering techniques starts with all records in one cluster and then try to split that |
| A. | Agglomerative. |
| B. | Divisive. |
| C. | Partition. |
| D. | Numeric |
| Answer» B. Divisive. | |
| 181. | Which of the following is a data set in the popular UCI machine-learning repository? |
| A. | CLARA. |
| B. | CACTUS. |
| C. | STIRR. |
| D. | MUSHROOM |
| Answer» D. MUSHROOM | |
| 182. | In algorithm each cluster is represented by the center of gravity of the cluster |
| A. | K-medoid |
| B. | K-means |
| C. | Stirr |
| D. | Rock |
| Answer» B. K-means | |
| 183. | In each cluster is represented by one of the objects of the cluster located near the center |
| A. | K-medoid |
| B. | K-means |
| C. | Stirr |
| D. | Rock |
| Answer» A. K-medoid | |
| 184. | Pick out a k-medoid algorithm |
| A. | DBSCAN |
| B. | BIRCH |
| C. | PAM |
| D. | CURE |
| Answer» C. PAM | |
| 185. | Pick out a hierarchical clustering algorithm |
| A. | DBSCAN |
| B. | CURE |
| C. | PAM |
| D. | BIRCH |
| Answer» D. BIRCH | |
| 186. | CLARANS stands for |
| A. | CLARA Net Server |
| B. | Clustering Large Application Range Network Search |
| C. | Clustering Large Applications based on Randomized Search |
| D. | Clustering Application Randomized Search |
| Answer» C. Clustering Large Applications based on Randomized Search | |
| 187. | BIRCH is a________ |
| A. | Agglomerative clustering algorithm |
| B. | Hierarchical algorithm |
| C. | Hierarchical-agglomerative algorithm |
| D. | Divisive |
| Answer» C. Hierarchical-agglomerative algorithm | |
| 188. | The cluster features of different subclusters are maintained in a tree called_________ |
| A. | CF tree |
| B. | FP tree |
| C. | FP growth tree |
| D. | B tree |
| Answer» A. CF tree | |
| 189. | The_______algorithm is based on the observation that the frequent sets are normally very few in number compared to the set of all itemsets |
| A. | A priori |
| B. | Clustering |
| C. | Association rule |
| D. | Partition |
| Answer» D. Partition | |
| 190. | The partition algorithm uses scans of the databases to discover all frequent sets |
| A. | Two |
| B. | Four |
| C. | Six |
| D. | Eight |
| Answer» A. Two | |
| 191. | The basic idea of the Apriori algorithm is to generate_____item sets of a particular size & scans the database |
| A. | Candidate |
| B. | Primary |
| C. | Secondary |
| D. | Superkey |
| Answer» A. Candidate | |
| 192. | is the most well-known association rule algorithm and is used in most commercial products |
| A. | Apriori algorithm |
| B. | Partition algorithm |
| C. | Distributed algorithm |
| D. | Pincer-search algorithm |
| Answer» A. Apriori algorithm | |
| 193. | An algorithm called________is used to generate the candidate item sets for each pass after the first |
| A. | Apriori |
| B. | Apriori-gen |
| C. | Sampling |
| D. | Partition |
| Answer» B. Apriori-gen | |
| 194. | The basic partition algorithm reduces the number of database scans to __________ & divides it into partitions |
| A. | One |
| B. | Two |
| C. | Three |
| D. | Four |
| Answer» B. Two | |
| 195. | and prediction may be viewed as types of classification |
| A. | Decision. |
| B. | Verification. |
| C. | Estimation. |
| D. | Illustration |
| Answer» C. Estimation. | |
| 196. | can be thought of as classifying an attribute value into one of a set of possible classes |
| A. | Estimation. |
| B. | Prediction. |
| C. | Identification. |
| D. | Clarification |
| Answer» B. Prediction. | |
| 197. | Prediction can be viewed as forecasting a value |
| A. | Non-continuous. |
| B. | Constant. |
| C. | Continuous. |
| D. | variable |
| Answer» C. Continuous. | |
| 198. | data consists of sample input data as well as the classification assignment for the data |
| A. | Missing. |
| B. | Measuring. |
| C. | Non-training. |
| D. | Training |
| Answer» B. Measuring. | |
| 199. | Rule based classification algorithms generate_________rule to perform the classification |
| A. | If-then |
| B. | While |
| C. | Do while |
| D. | Switch |
| Answer» A. If-then | |
| 201. | The human brain consists of a network of . |
| A. | Neurons |
| B. | Cells |
| C. | Tissue |
| D. | Muscles |
| Answer» A. Neurons | |
| 202. | Each neuron is made up of a number of nerve fibers called________. |
| A. | Electrons |
| B. | Molecules |
| C. | Atoms |
| D. | Dendrites |
| Answer» D. Dendrites | |
| 203. | The__________is a long, single fiber that originates from the cell body. |
| A. | Axon |
| B. | Neuron |
| C. | Dendrites |
| D. | Strands |
| Answer» A. Axon | |
| 204. | A single axon makes of synapses with other neurons. |
| A. | Ones |
| B. | Hundreds |
| C. | Thousands |
| D. | Millions |
| Answer» C. Thousands | |
| 205. | is a complex chemical process in neural networks. |
| A. | Receiving process |
| B. | Sending process |
| C. | Transmission process |
| D. | Switching process |
| Answer» C. Transmission process | |
| 206. | Is the connectivity of the neuron that give simple devices their real power? |
| A. | Water |
| B. | Air |
| C. | Power |
| D. | Fire |
| Answer» D. Fire | |
| 207. | Are highly simplified models of biological neurons? |
| A. | Artificial neurons |
| B. | Computational neurons |
| C. | Biological neurons |
| D. | Technological neurons |
| Answer» A. Artificial neurons | |
| 208. | The biological neuron’s__________is a continuous function rather than a step function. |
| A. | Read. |
| B. | Write. |
| C. | Output. |
| D. | Input |
| Answer» C. Output. | |
| 209. | The threshold function is replaced by continuousfunctions called______functions. |
| A. | Activation. |
| B. | Deactivation. |
| C. | Dynamic. |
| D. | Standard |
| Answer» A. Activation. | |
| 210. | The sigmoid function also knows as __functions. |
| A. | Regression |
| B. | Logisti |
| C. | C Probability |
| D. | Neural |
| Answer» B. Logisti | |
| 211. | MLP stands for . |
| A. | Mono layer perception |
| B. | Many layer perception |
| C. | More layer perception |
| D. | Multi-layer perception |
| Answer» D. Multi-layer perception | |
| 212. | In a feed- forward networks, the connections between layers are________from input to output. |
| A. | Bidirectional |
| B. | Unidirectional |
| C. | Multidirectional |
| D. | Directional |
| Answer» B. Unidirectional | |
| 213. | The network topology is constrained to be . |
| A. | Feed forward |
| B. | Feed backward |
| C. | Feed free |
| D. | Feed busy |
| Answer» A. Feed forward | |
| 214. | RBF stands for . |
| A. | Radial basis function |
| B. | Radial bio function |
| C. | Radial big function |
| D. | Radial bi function |
| Answer» A. Radial basis function | |
| 215. | RBF have only hidden layer. |
| A. | Four |
| B. | Three |
| C. | Two |
| D. | One |
| Answer» D. One | |
| 216. | RBF hidden layer units have a receptive field which has a________; that is, a particular input value at which they have a maximal output. |
| A. | Top |
| B. | Bottom |
| C. | Centre |
| D. | Border |
| Answer» C. Centre | |
| 217. | Training may be used when a clear link between input data sets and target output values does not exist. |
| A. | Competitive |
| B. | Perception |
| C. | Supervise |
| D. | D Unsupervised |
| Answer» D. D Unsupervised | |
| 218. | Employs the supervised mode of learning. |
| A. | RBF |
| B. | MLP |
| C. | MLP & RBF |
| D. | ANN |
| Answer» C. MLP & RBF | |
| 219. | Design involves deciding on their centers and the sharpness of their Gaussians. |
| A. | DR |
| B. | AND |
| C. | XOR |
| D. | RBF |
| Answer» D. RBF | |
| 220. | is the most widely applied neural network technique. |
| A. | ABC |
| B. | PLM |
| C. | LMP |
| D. | MLP |
| Answer» D. MLP | |
| 221. | SOM is an acronym of . |
| A. | Self-organizing map |
| B. | Self-origin map |
| C. | Single organizing map |
| D. | Simple origin map |
| Answer» A. Self-organizing map | |
| 222. | Is one of the most popular models in the unsupervised framework? |
| A. | SOM |
| B. | SAM |
| C. | OSM |
| D. | MSO |
| Answer» A. SOM | |
| 223. | The actual amount of reduction at each learning step may be guided by_________. |
| A. | learning cost |
| B. | learning level |
| C. | learning rate |
| D. | learning time |
| Answer» C. learning rate | |
| 224. | The SOM was a neural network model developed by__________. |
| A. | Simon King |
| B. | Teuvokohonen |
| C. | Tomoki Toda |
| D. | Julia |
| Answer» B. Teuvokohonen | |
| 226. | Investment analysis used in neural networks is to predict the movement of_________from previous data. | |
| A. | Engines | |
| B. | Stock | |
| C. | Patterns | |
| D. | Models | |
| Answer» B. Stock | ||
| 227. | SOMs are used to cluster a specific dataset containing information about the patient’s drugs etc. |
| A. | Physical |
| B. | Logical |
| C. | Medical |
| D. | Technical |
| Answer» C. Medical | |
| 228. | GA stands for ____ . |
| A. | Genetic algorithm |
| B. | Gene algorithm |
| C. | General algorithm |
| D. | Geo algorithm |
| Answer» A. Genetic algorithm | |
| 229. | GA was introduced in the year________. |
| A. | 1955 |
| B. | 1965 |
| C. | 1975 |
| D. | 1985 |
| Answer» C. 1975 | |
| 230. | Genetic algorithms are search algorithms based on the mechanics of natural . |
| A. | Systems |
| B. | Genetics |
| C. | Logistics |
| D. | Statistics |
| Answer» B. Genetics | |
| 231. | The RSES system was developed in . |
| A. | Poland |
| B. | Italy |
| C. | Englan |
| D. | D America |
| Answer» A. Poland | |
| 232. | Crossover is used to . |
| A. | Recombine the population’s genetic material |
| B. | Introduce new genetic structures in the population |
| C. | To modify the population’s genetic material |
| D. | All of the above |
| Answer» A. Recombine the population’s genetic material | |
| 233. | The mutation operator . |
| A. | Recombine the population’s genetic material |
| B. | Introduce new genetic structures in the population |
| C. | To modify the population’s genetic material |
| D. | All of the above |
| Answer» B. Introduce new genetic structures in the population | |
| 234. | Which of the following is an operation in genetic algorithm? |
| A. | Inversion |
| B. | Dominance |
| C. | Genetic edge recombination |
| D. | All of the above |
| Answer» D. All of the above | |
| 235. | ____________ is a system created for rule induction. |
| A. | RBS |
| B. | CBS |
| C. | DBS |
| D. | LERS |
| Answer» D. LERS | |
| 236. | NLP stands for____________. |
| A. | Non Language Process |
| B. | Nature Level Program |
| C. | Natural Language Page |
| D. | Natural Language Processing |
| Answer» D. Natural Language Processing | |
| 237. | Web content mining describes the discovery of useful information from the contents. |
| A. | Text |
| B. | Web |
| C. | Page |
| D. | Level |
| Answer» B. Web | |
| 238. | Research on mining multi-types of data is termed as__________data. |
| A. | Graphics |
| B. | Multimedia |
| C. | Meta |
| D. | Digital |
| Answer» B. Multimedia | |
| 239. | Mining is concerned with discovering the model underlying the link structures of the web. |
| A. | Data structure |
| B. | Web structure |
| C. | Text structure |
| D. | Image structure |
| Answer» B. Web structure | |
| 240. | is the way of studying the web link structure. |
| A. | Computer network |
| B. | Physical network |
| C. | Social network |
| D. | Logical network |
| Answer» C. Social network | |
| 241. | The__________propose a measure of standing a node based on path counting. |
| A. | Open we |
| B. | B Close web |
| C. | Link web |
| D. | Hidden web |
| Answer» B. B Close web | |
| 242. | In web mining, is used to find natural groupings of users, pages, etc. |
| A. | Clustering |
| B. | Associations |
| C. | Sequential analysis |
| D. | Classification |
| Answer» A. Clustering | |
| 243. | In web mining, is used to know the order in which URLs tend to be accessed. |
| A. | clustering |
| B. | associations |
| C. | sequential analysis |
| D. | classification |
| Answer» C. sequential analysis | |
| 244. | In web mining, is used to know which URLs tend to be requested together. |
| A. | Clustering |
| B. | Associations |
| C. | Sequential analysis |
| D. | Classification |
| Answer» B. Associations | |
| 245. | Describes the discovery of useful information from the web contents. |
| A. | Web content mining |
| B. | Web structure mining |
| C. | Web usage mining |
| D. | All of the above |
| Answer» A. Web content mining | |
| 246. | is concerned with discovering the model underlying the link structures of the web. |
| A. | Web content mining. |
| B. | Web structure mining. |
| C. | Web usage mining. |
| D. | All of the above |
| Answer» B. Web structure mining. | |
| 247. | The__________engine for a data warehouse supports query-triggered usage of data. |
| A. | NNTP |
| B. | SMTP |
| C. | OLAP |
| D. | POP |
| Answer» C. OLAP | |
| 248. | displays of data such as maps, charts and other graphical representation allow data to be presented compactly to the users. |
| A. | Hidden |
| B. | Visual |
| C. | Obscure |
| D. | D Concealed |
| Answer» B. Visual | |
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