STUDENT
AI - NLP
Total Q: 45
Time: 50 Mins
Q 1.
Which algorithms result in two things, a vocabulary of words and frequency of the words in the corpus?
Sentence segmentation
Tokenisation
Text normalisation
Bag of words
Q 2.
Which of the following is the first step in the stages of Natural Language Processing (NLP)?
Semantic Analysis
Lexical Analysis
Pragmatic Analysis
Discourse Integration
Q 3.
Which domain of AI is used for interacting with virtual assistants such as Siri and Alexa?
Machine Learning (ML)
Computer Vision (CV)
Natural Language Processing (NLP)
Technical Vision (TV)
Q 4.
Which of the following is the type of data used by NLP applications?
Images
Numerical data
Graphical data
Text and Speech
Q 5.
_______________ helps in assigning a predefined category to a document, organize it in such a way that helps customers to find information they want. For example spam filtering in email, auto tagging on social media, categorization of news articles etc.
Text Classification
Automatic Summarization
Sentiment Analysis
Virtual Assistants
Q 6.
Which of the following is true about Term Frequency-Inverse Document Frequency (TF-IDF)?
It removes all stopwords
It increases the weight of frequently occurring terms in all documents
It reduces the impact of commonly occurring terms across many documents
It counts total number of characters in a document
Q 7.
Identify the given Chat bot type:
It learns from its environment and experience. It also builds on its capabilities based on the knowledge. These can collaborate with humans, working along-side them and learning from their behavior.
Script Bot
Smart Bot
AI Bot
Chat Bot
Q 8.
For _________ the whole corpus is divided into sentences. Each sentence is taken as a different data so now the whole corpus gets reduced to sentences.
Text Regulation
Sentence Segmentation
Tokenisation
Stemming
Q 9.
Which of the following applications is not associated with Natural Language Processing (NLP)?
Sentiment Analysis
Spam Filtering in emails
Speech Recognition
Stock Market Analysis
Q 10.
This real life application of NLP is used to provide an overview of a news item or blog post, while avoiding redundancy from multiple sources and maximising the diversity of content obtained. Which is this application?
Chatbot
Virtual Assistant
Sentiment Analysis
Automatic Summarisation
Q 11.
Look at the two documents:
Document 1: We can use health chatbots for treating stress.
Document 2: We can use NLP to create chatbots and we will be making health chatbots now!
Identify the term in which we can categorise the word 'Chatbots'
Stop word
Rare word
Frequent word
Removable word
Q 12.
Aditi, a student of class XII developed a chatbot that clarifies the doubts of Economics students. She trained the software with lots of data sets catering to all difficulty levels. If any student would type or ask questions related to Economics, the software would give an instant reply. Identify the domain of AI in the given scenario.
Computer Vision
Data Science
Natural Language Processing
None of these
Q 13.
With reference to NLP, consider the following plot of occurrence of words versus their value:
In the given graph, X represents :
Rare / valuable words
Punctuation words
Popular words
Pronoun
Q 14.
In Bag of Words, what are document vectors used for?
To remove punctuation
To compress text
To show word frequency in each document
To translate text
Q 15.
Which step in text normalization involves breaking down text into individual words, numbers, or symbols?
Lemmatization
Stemming
Tokenization
Sentence Segmentation
Q 16.
Which of the following is a feature of document classification ?
Helps in classifying the type and genre of a document.
Helps in creating a document.
Helps to display important information of a corpus.
Helps in including the necessary words in the text body.
Q 17.
Bag of Words is a ______ model which helps in extracting features out of the text which can be helpful in machine learning algorithms.
Data Science (DS)
Virtual Reality (VR)
Natural Language Processing (NLP)
Computer Vision (CV)
Q 18.
What is the role of modelling in an NLP based AI model?
Modelling in NLP helps in processing of AI model
Modelling is required to make an AI model
In NLP, modelling requires data pre-processing only after which the data is fed to the machine.
Modelling is used in simplification of data acquisition
Q 19.
__________ is a term used for any word or number or special character occurring in a sentence.
Token
Punctuator
Stop Word
Word
Q 20.
Which of the following words represent an example of a lemma resulting from lemmatisation for "caring" in context to Natural Language Processing (NLP) ?
Care
Cared
Cares
Car
Q 21.
The term Sentence Segmentation is
the whole corpus is divided into sentences
to undergo several steps to normalise the text to a lower level
in which each sentence is then further divided into tokens
the process in which the affixes of words are removed
Q 22.
___________ is an example of Applications of Natural Language Processing.
Evaluation
Automatic Summarization
Deep Learning
Problem Scoping
Q 23.
Identify the given Chat bot type:
Bots which are deployed in the customer care section of various companies.
Script Bot
Smart Bot
AI Bot
Chat Bot
Q 24.
_________________is the sub-field of AI that is focused on enabling computers to understand and process human languages.
Deep Learning
Machine Learning
NLP
Data Sciences
Q 25.
Which feature of NLP makes it possible to assign predefined categories to a document and organize it to help you find the information you need or simplify some activities?
Virtual Assistants
Sentiment Analysis
Text classification
Automatic Summarization
Q 26.
Ayushi was learning about NLP. She wanted to know the term used for the whole textual data from all the documents altogether. Help her in identifying the term used for it.
Corpus
Sentence Segmentation
Token
Punctuator
Q 27.
What is the full form of TF-IDF?
Term Frequency - Inverse Document Frequency
Term Frequency - Introvert Document Frequency
Term Frequent - Inverse Document Frequent
Term Frequent - Introvert Document Frequent
Q 28.
What is the analogy with the statement:
2+3 = 3+2
Different syntax, same semantics
Different semantics, same syntax
Different syntax, different semantics
None of the above
Q 29.
Search engines not only predict what popular searches may apply to your query as you start typing, but it looks at the whole picture and recognizes what you're trying to say rather than the exact search words. This is an example of
Computer Vision
Data Sciences
Natural Language Processing
Natural Language Understanding
Q 30.
A corpus contains 4 documents in which the word 'diet' was appearing once in document1. Identify the term in which we can categorise the word 'diet'.
Stop word
Rare word
Frequent word
Removable word
Q 31.
What is the key difference between stemming and lemmatization?
Lemmatization does not remove affixes
Stemming produces only meaningful words
Lemmatization guarantees meaningful root words
Stemming takes longer to execute
Q 32.
What does the 'bag' in Bag of Words signify in NLP?
Words are processed in the order of appearance
Only grammatical words are included
The sequence of words is not important
Only nouns and verbs are considered
Q 33.
____ is the process of finding instances of real-world objects in images or videos.
Instance segmentation
Object detection
Classification
Image segmentation
Q 34.
Which feature of NLP helps in understanding the emotions of the people mentioned with the feedback?
Virtual Assistants
Sentiment Analysis
Text classification
Automatic Summarization
Q 35.
Smart Assistants such as Alexa, Siri are the examples of :
Natural Language Processing
Data Science
Machine Learning
Computer Vision
Q 36.
Email filters, spam filters, smart assistants are the examples of :
Pocket Assistants
CV
NLP
Evaluation
Q 37.
What is the purpose of semantic analysis in NLP?
To create tokens from sentences
To convert text to lower case
To check the meaning of each word and phrase
To remove special characters
Q 38.
Under __________, every word, number and special character is considered separately and each of them is now a separate token.
Sentence Segmentation
Stemming
Tokenisation
Document Vector
Q 39.
Select the correct features of Smart Bot :
Smart-bots are flexible and powerful
Coding is required to take this up on board
Smart bots work on bigger databases and other resources directly
All of these
Q 40.
A corpus contains 12 documents. How many document vectors will be there for that corpus?
12
1
24
1/12
Q 41.
Identify the type of chatbot with the information given below: These bots work on pre-programmed instructions inside the application/machine and are generally easy to develop. They are deployed in the customer care section of various companies. Their job is to answer some basic queries that they are coded for and connect them to human executives once they are unable to handle the conversation.
Script Bot
Smart Bot
AI Bot
Chat Bot
Q 42.
Which stage of NLP focuses on understanding the intended meaning beyond literal interpretation?
Lexical Analysis
Syntactic Analysis
Semantic Analysis
Pragmatic Analysis
Q 43.
Which of the following is NOT typically removed during the stopword removal step?
"the"
"is"
"and"
"health"
Q 44.
In NLP, what is the main function of discourse integration?
Checking grammar rules
Making sense of a sentence's meaning in isolation
Establishing context between sentences
Removing stopwords from text
Q 45.
What do you mean by syntax of a language?
Meaning of a sentence
Grammatical structure of a sentence
Semantics of a sentence
Synonym of a sentence