The Database Quiz

31 Questions | Total Attempts: 217

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The Database Quiz

A database is a structured set of electronic data held in a computer and not physically tangible. The database quiz below is for all the techies who are revising for their database exams. Take it up and all the best.


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Questions and Answers
  • 1. 
      Text analytics is the subset of text mining that handles information retrieval and extraction, plus data mining
    • A. 

      True

    • B. 

      False

  • 2. 
      In text mining, inputs to the process include unstructured data such as Word documents, PDF files, text excerpts, e-mail and XML files
    • A. 

      True

    • B. 

      False

  • 3. 
     4.  During information extraction, entity recognition (the recognition of names of people and organizations) takes place after relationship extraction
    • A. 

      True

    • B. 

      False

  • 4. 
    Categorization and clustering of documents during text mining differ only in the preselection of categories
    • A. 

      True

    • B. 

      False

  • 5. 
      Articles and auxiliary verbs are assigned little value in text mining and are usually filtered out
    • A. 

      True

    • B. 

      False

  • 6. 
     The bag-of-words model is appropriate for spam detection but not for text analytics. 
    • A. 

      True

    • B. 

      False

  • 7. 
    Chinese, Japanese, and Thai have features that make them more difficult candidates for natural language processing
    • A. 

      True

    • B. 

      False

  • 8. 
      Regional accents present challenges for natural language processing
    • A. 

      True

    • B. 

      False

  • 9. 
     Detecting lies from text transcripts of conversations is a future goal of text mining as current systems achieve only 50% accuracy of detection. 
    • A. 

      True

    • B. 

      False

  • 10. 
    .  In text mining, creating the term-document matrix includes all the terms that are included in all documents, making for huge matrices only manageable on computers
    • A. 

      True

    • B. 

      False

  • 11. 
      In text mining, if an association between two concepts has 7% support, it means that 7% of the documents had both concepts represented in the same document
    • A. 

      True

    • B. 

      False

  • 12. 
    In sentiment analysis, sentiment suggests a transient, temporary opinion reflective of one's feelings
    • A. 

      True

    • B. 

      False

  • 13. 
     Current use of sentiment analysis in voice of the customer applications allows companies to change their products or services in real time in response to customer sentiment. 
    • A. 

      True

    • B. 

      False

  • 14. 
      In sentiment analysis, it is hard to classify some subjects such as news as good or bad, but easier to classify others, e.g., movie reviews, in the same way. 
    • A. 

      True

    • B. 

      False

  • 15. 
      The linguistic approach to speech handles processes elements such as intensity, pitch and jitter from speech recorded on audio
    • A. 

      True

    • B. 

      False

  • 16. 
     According to a study by Merrill Lynch and Gartner, what percentage of all corporate data is captured and stored in some sort of unstructured form
    • A. 

      15%

    • B. 

      85%

    • C. 

      25%

    • D. 

      75%

  • 17. 
      Which of these applications will derive the LEAST benefit from text mining
    • A. 

      Sales transaction file

    • B. 

      patent description files

    • C. 

      customer comment files

    • D. 

      patients' medical files

  • 18. 
    .  In text mining, stemming is the process of 
    • A. 

      creating new branches or stems of recorded paragraphs.

    • B. 

      ) reducing multiple words to their base or root

    • C. 

      categorizing a block of text in a sentence.

    • D. 

      transforming the term-by-document matrix to a manageable size.

  • 19. 
    .  In text mining, tokenizing is the process of 
    • A. 

      transforming the term-by-document matrix to a manageable size

    • B. 

      reducing multiple words to their base or roo

    • C. 

      categorizing a block of text in a sentence

    • D. 

      creating new branches or stems of recorded paragraphs

  • 20. 
     All of the following are challenges associated with natural language processing EXCEPT 
    • A. 

      dividing up a text into individual words in English.

    • B. 

      understanding the context in which something is said

    • C. 

      recognizing typographical or grammatical errors in texts.

    • D. 

      distinguishing between words that have more than one meaning

  • 21. 
    .  What application is MOST dependent on text analysis of transcribed sales call center notes and voice conversations with customers? 
    • A. 

      CRM

    • B. 

      OLAP

    • C. 

      Finance

    • D. 

      ERP

  • 22. 
    .  In text mining, which of the following methods is NOT used to reduce the size of a sparse matrix? 
    • A. 

      eliminating rarely occurring terms

    • B. 

      using singular value decomposition

    • C. 

      using a domain expert

    • D. 

      normalizing word frequencies

  • 23. 
     What data discovery process, whereby objects are categorized into predetermined groups, is used in text mining? 
    • A. 

      classification

    • B. 

      trend analysis

    • C. 

      association

    • D. 

      clustering

  • 24. 
      Sentiment classification usually covers all the following issues EXCEPT 
    • A. 

      range of polarity (e.g., star ratings for hotels and for restaurants).

    • B. 

      classes of sentiment (e.g., positive versus negative).

    • C. 

      range in strength of opinion

    • D. 

      biometric identification of the consumer expressing the sentiment.

  • 25. 
    .  In sentiment analysis, which of the following is an implicit opinion?
    • A. 

      The cruise we went on last summer was a disaster

    • B. 

      Our new mayor is great for the city.

    • C. 

      The customer service I got for my TV was laughable.

    • D. 

      The hotel we stayed in was terrible.

  • 26. 
      What do voice of the market (VOM) applications of sentiment analysis do? 
    • A. 

      They examine the "market of ideas" in politics

    • B. 

      They examine employee sentiment in the organization.

    • C. 

      They examine customer sentiment at the aggregate level.

    • D. 

      They examine the stock market for trends.

  • 27. 
    .  How is objectivity handled in sentiment analysis? 
    • A. 

      It is clarified with the customer who expressed it

    • B. 

      It is incorporated as a type of sentiment.

    • C. 

      It is identified and removed as facts are not sentiment

    • D. 

      It is ignored because it does not appear in customer sentiment

  • 28. 
     Identifying the target of an expressed sentiment is difficult for all the following reasons EXCEPT 
    • A. 

      the review may not be directly connected to the target through the topic name.

    • B. 

      sometimes there are multiple targets expressed in a sentiment.

    • C. 

      strong sentiments may be generated by a computer, not a person.

    • D. 

      blogs and articles with the sentiment may be general in nature.

  • 29. 
    .  In text analysis, what is a lexicon? 
    • A. 

      a catalog of words, their synonyms, and their meanings

    • B. 

      a catalog of customers, their words, and phrase

    • C. 

      a catalog of customers, products, words, and phrase

    • D. 

      a catalog of letters, words, phrases and sentences

  • 30. 
      What types of documents are BEST suited to semantic labeling and aggregation to determine sentiment orientation? 
    • A. 

      collections of documents

    • B. 

      Medium- to large-sized documents

    • C. 

      large-sized documents

    • D. 

      small- to medium-sized documents

  • 31. 
     Inputs to speech analytics include all of the following EXCEPT 
    • A. 

      Written transcripts of calls to service centers

    • B. 

      recorded conversations of customer call-ins.

    • C. 

      videos of customer focus groups.

    • D. 

      Live customer interactions with service representatives