MCQs

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Overall MCQs for Reference

What is spaCy primarily used for in Machine Learning?
a. Image Recognition
b. Natural Language Processing
c. Speech Synthesis
d. Video Analysis
Answer: b. Natural Language Processinggeeksforgeeks+1
Which language is spaCy implemented in?
a. Java
b. C++
c. Python
d. Ruby
Answer: c. Pythonspacy+1
Which of the following is NOT a core feature of spaCy?
a. Tokenization
b. POS tagging
c. Sentiment analysis (built-in)
d. Named Entity Recognition
Answer: c. Sentiment analysis (built-in)spacy+1
What is the main container object in spaCy for storing processed text?
a. Document
b. Doc
c. Text
d. Sentence
Answer: b. Docgeeksforgeeks
What does spaCy’s tokenization typically return?
a. List of sentences
b. List of tokens
c. List of paragraphs
d. List of stop words
Answer: b. List of tokensspacy+2
How do you load the default English model in spaCy?
a. spacy.Model(“en”)
b. spacy.load(“en_core_web_sm”)
c. nlp = English()
d. spacy.load(“english-default”)
Answer: b. spacy.load(“en_core_web_sm”)spacy+1
Which method is used to process text input in spaCy’s pipeline?
a. nlp()
b. process_text()
c. run_nlp()
d. tokenize()
Answer: a. nlp()geeksforgeeks+1
What is the purpose of the Matcher class in spaCy?
a. POS tagging
b. Rule-based pattern matching
c. Sentiment scoring
d. Translation
Answer: b. Rule-based pattern matchingspacy+1
Which spaCy object represents a sequence of tokens, such as a phrase or a sentence?
a. Token
b. Span
c. Word
d. Sentence
Answer: b. Spangeeksforgeeks+1
Which linguistic annotation is NOT directly available in spaCy?
a. Lemmatization
b. Syllabification
c. Dependency parsing
d. Part-of-Speech tagging
Answer: b. Syllabificationdatacamp+1
The built-in Named Entity Recognizer in spaCy can identify:
a. Only locations
b. Only people
c. Predefined entity types
d. Arbitrary word groups
Answer: c. Predefined entity typesspacy+2
How can you access the part-of-speech tag for a token in spaCy?
a. token.pos
b. token.pos_
c. token.get_pos()
d. token.POS
Answer: b. token.pos_geeksforgeeks
What is the method used for batch processing of documents in spaCy?
a. nlp.batch()
b. nlp.pipe()
c. nlp.process_all()
d. nlp.pipelined()
Answer: b. nlp.pipe()cybrosys
Which attribute holds the lemma of a token in spaCy?
a. token.lemma_
b. token.stem_
c. token.base
d. token.lemma()
Answer: a. token.lemma_datacamp+1
For what task does spaCy use the dependency parser?
a. Text classification
b. Grammatical structure analysis
c. Stemming
d. Stop word filtering
Answer: b. Grammatical structure analysiscybrosys+1
Which model size is typically the smallest in spaCy’s English models?
a. en_core_web_md
b. en_core_web_lg
c. en_core_web_sm
d. en_core_web_xl
Answer: c. en_core_web_smspacy
What does POS stand for in spaCy?
a. Position
b. Pre-Oriented Syntax
c. Part-of-Speech
d. Pieces of Structure
Answer: c. Part-of-Speechspacy+1
How can you disable certain pipeline components in spaCy?
a. Using the ‘disable’ argument in spacy.load
b. Using nlp.disable_component()
c. There is no way
d. By uninstalling spaCy
Answer: a. Using the ‘disable’ argument in spacy.loadcybrosys
Which vector functionality does spaCy offer for semantic similarity?
a. tf-idf
b. Doc2Vec
c. Word vectors
d. Lexical casting
Answer: c. Word vectorsspacy+2
Which type of token attribute does token.is_alpha return?
a. String
b. Boolean
c. Integer
d. List
Answer: b. Booleangeeksforgeeks+1
What is the result of nlp(“Hello world!”) in spaCy?
a. List of lemmatized words
b. A processed Doc object
c. A single Token
d. None
Answer: b. A processed Doc objectgeeksforgeeks
Which pipeline component in spaCy identifies the grammatical roles of tokens?
a. NER
b. Tokenizer
c. Tagger
d. Parser
Answer: d. Parserspacy+1
What does token.like_num indicate in spaCy?
a. Whether a token is a number
b. Whether a token is a noun
c. If a token is a named entity
d. If a token is a verb
Answer: a. Whether a token is a numberspacy+1
In spaCy, how to get the string representation of a token?
a. token.to_string()
b. str(token)
c. token.text
d. token.value
Answer: c. token.textspacy+1
What is spaCy’s default sentence segmentation component called?
a. Segmenter
b. Sentencizer
c. Parser
d. Splitter
Answer: c. Parsergeeksforgeeks+1
Which function is used for visualization in spaCy?
a. spacy.display()
b. displacy.render()
c. plot_nlp()
d. spacy.show()
Answer: b. displacy.render()spacy+1
What is the main purpose of spaCy’s EntityRuler?
a. Rule-based entity recognition
b. Spell correction
c. Lemmatization
d. POS tagging
Answer: a. Rule-based entity recognitiongeeksforgeeks+1
What attribute returns the named entity label of a span?
a. span.text
b. span.ent_type_
c. span.is_entity
d. span.entity_label
Answer: b. span.ent_type_spacy+1
spaCy word vectors are mainly used for:
a. Grammar correction
b. Semantic similarity
c. Stemming
d. POS tagging
Answer: b. Semantic similaritycybrosys+1
spaCy supports loading custom:
a. Word embeddings
b. Spelling rules
c. JSON files only
d. POS taggers only
Answer: a. Word embeddingscybrosys
To improve efficiency when processing large texts, spaCy recommends:
a. Using GPU exclusively
b. Disabling unused pipeline components
c. Increasing pipeline depth
d. Disabling language models
Answer: b. Disabling unused pipeline componentscybrosys
Which method in spaCy helps in extracting substrings as object?
a. doc.substring()
b. doc.char_span()
c. doc.extract()
d. doc.string_span()
Answer: b. doc.char_span()geeksforgeeks
In spaCy, what is the function of the .is_stop attribute for tokens?
a. Checks if the token is a stop word
b. Marks the end of a sentence
c. Checks punctuation
d. Marks the start of a document
Answer: a. Checks if the token is a stop wordspacy+1
spaCy supports which kind of pipeline customization?
a. Adding custom components
b. Only removing components
c. Only built-in pipeline fixed
d. No customization
Answer: a. Adding custom componentscybrosys+1
What technique does spaCy use for sentence boundary detection?
a. Rule-based only
b. Machine learning models
c. Regular expressions
d. Only word count
Answer: b. Machine learning modelsdatacamp+1
spaCy models are optimized for:
a. Production usability and speed
b. Training from scratch
c. Ultra-high resource use
d. Only academic experiments
Answer: a. Production usability and speedcybrosys+1
Which component would you add in a spaCy pipeline for custom phrase detection?
a. EntityRuler
b. Sentencizer
c. Similarity
d. TextRank
Answer: a. EntityRulergeeksforgeeks+1
In spaCy, pipeline components process text in which order?
a. Random
b. Fixed pipeline order
c. Parallel
d. Alphabetical
Answer: b. Fixed pipeline orderspacy+1
spaCy’s NER model can be retrained for:
a. Only English
b. Custom entity types
c. POS tagging
d. Tokenization only
Answer: b. Custom entity typesgeeksforgeeks+1
Which format is primarily used by spaCy for model serialization?
a. .json
b. .bin
c. .pkl
d. .csv
Answer: b. .binspacy+1
What does nlp.pipe() return?
a. Generator for processed Docs
b. List of strings
c. List of sentences
d. List of Tokens
Answer: a. Generator for processed Docscybrosys+1
In spaCy, which attribute would you use to get the start index of a span?
a. span.start
b. span.init
c. span.idx
d. span.beg
Answer: a. span.startgeeksforgeeks
To save a trained spaCy pipeline, which method is used?
a. nlp.store()
b. nlp.save()
c. nlp.to_disk()
d. nlp.write()
Answer: c. nlp.to_disk()spacy+1
spaCy distinguishes between:
a. Only English and French models
b. Pre-trained and blank models
c. Only tokenizers
d. Only pre-trained models
Answer: b. Pre-trained and blank modelsspacy+1
spaCy visualizations are mainly provided using:
a. displacy module
b. matplotlib
c. numpy
d. seaborn
Answer: a. displacy modulegeeksforgeeks+1
How are multi-word tokens handled in spaCy?
a. Always as a single token
b. Split per rules and exceptions
c. Not supported
d. Only for NER
Answer: b. Split per rules and exceptionsdatacamp+1
Which file provides pipeline configuration in saved spaCy models?
a. config.json
b. meta.yaml
c. pipeline.bin
d. pipeline.txt
Answer: a. config.jsonspacy+1
In spaCy, which of the following is a reserved factory name for pipeline component creation?
a. get_component
b. add_factory
c. create_component
d. factory
Answer: d. factoryspacy
To load only the tokenizer from an English pipeline:
a. spacy.Tokenizer(“en_core_web_sm”)
b. spacy.load(“en_core_web_sm”, disable=[“parser”, “ner”, “tagger”])
c. nlp.token_only()
d. Only English()
Answer: b. spacy.load(“en_core_web_sm”, disable=[“parser”, “ner”, “tagger”])cybrosys+1
What is a practical use case for spaCy’s vector similarities?
a. Grammar correction
b. Sentence clustering
c. HTML parsing
d. Sound classification
Answer: b. Sentence clusteringcybrosys+1

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