MCQs

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Overall MCQs for Reference

What is spaCy primarily used for?
a. Web development
b. Natural Language Processing
c. Image recognition
d. Data warehousing
Answer: b. Natural Language Processingdevopsschool+1
Which programming language is spaCy written in?
a. Java
b. Python
c. C#
d. R
Answer: b. Pythontutorialspoint+1
Who developed the spaCy library?
a. Guido van Rossum
b. Matthew Honnibal and Ines Montani
c. Elon Musk
d. Jeff Dean
Answer: b. Matthew Honnibal and Ines Montanigeeksforgeeks+1
What does spaCy’s tokenization process do?
a. Encrypts text
b. Breaks text into individual words and punctuation
c. Counts the number of words
d. Translates text to another language
Answer: b. Breaks text into individual words and punctuationspacy+1
Which method is used for tokenizing text in spaCy?
a. tokenize_text()
b. nlp.tokenize()
c. spacy.tokenize()
d. nlp()
Answer: d. nlp()geeksforgeeks+1
Which spaCy component assigns grammatical tags to words?
a. EntityRecognizer
b. POS Tagger
c. Lemmatizer
d. Tokenizer
Answer: b. POS Taggerspacy+1
What is Named Entity Recognition (NER) in spaCy?
a. Translating text
b. Generating random entities
c. Removing entities from text
d. Extracting and classifying entities from text
Answer: d. Extracting and classifying entities from textwayground+2
How does spaCy identify named entities?
a. By manual input
b. Rule-based and statistical models
c. Only ML algorithms
d. Random guessing
Answer: b. Rule-based and statistical modelswayground+1
Which of the following is a feature of spaCy?
a. Real-time video rendering
b. Part-of-speech tagging
c. Hardware monitoring
d. Data backup
Answer: b. Part-of-speech taggingtutorialspoint+2
What are word embeddings in spaCy?
a. Audio of words
b. Random characters
c. Image representations
d. Dense vectors capturing semantics
Answer: d. Dense vectors capturing semanticsdevopsschool+1
What is lemmatization in spaCy?
a. Finding sentence boundaries
b. Assigning base forms of words
c. Creating images
d. Highlighting errors
Answer: b. Assigning base forms of wordsspacy+1
Which spaCy component can you use for rule-based pattern matching?
a. Matcher
b. Tokenizer
c. Classifier
d. Parser
Answer: a. Matcherdevopsschool+1
Which object in spaCy represents a processed document?
a. Doc
b. Token
c. Span
d. List
Answer: a. Docgeeksforgeeks+2
SpaCy’s pipeline components are applied in which order?
a. Randomly
b. In reverse
c. Sequentially
d. Based on model size
Answer: c. Sequentiallyspacy+1
How do you load a pre-trained English model in spaCy?
a. spacy.load(“en_core_web_sm”)
b. spaCy.import(“english”)
c. load.nlp(“en”)
d. spacy.English()
Answer: a. spacy.load(“en_core_web_sm”)tutorialspoint+1
Which of the following is NOT a built-in pipeline component in spaCy?
a. Tokenizer
b. EntityRecognizer
c. Sentencizer
d. Synthesizer
Answer: d. Synthesizerdevopsschool+1
What type of model does spaCy use for POS tagging?
a. Rule-based
b. Statistical
c. Generative adversarial
d. Decision tree
Answer: b. Statisticalspacy+1
How can you add custom metadata to spaCy’s Doc object?
a. doc.add_data()
b. doc..custom_attr
c. doc.meta
d. doc.attribute
Answer: b. doc..custom_attrspacy
What does the nlp object in spaCy represent?
a. The input sentence
b. A loaded language pipeline
c. The system clock
d. Image processor
Answer: b. A loaded language pipelinetutorialspoint+1
How is dependency parsing useful in spaCy?
a. Detects hardware failures
b. Analyzes grammatical structure
c. Signals end of document
d. Runs embedded code
Answer: b. Analyzes grammatical structuredevopsschool+1
Which is used for visualization in spaCy?
a. spacy.dispacy
b. spacy.plotter
c. spacy.graphviz
d. spacy.show()
Answer: a. spacy.dispacydevopsschool+1
What is a Span in spaCy?
a. Processed document
b. A contiguous sequence of tokens
c. A neural network layer
d. Hardware address
Answer: b. A contiguous sequence of tokensspacy+1
How can rules be combined with statistical models in spaCy?
a. Through the EntityRuler
b. By retraining Tokenizer
c. By Classifier only
d. It’s not possible
Answer: a. Through the EntityRulerdevopsschool+1
Which object denotes a single word, punctuation, or symbol in spaCy?
a. Doc
b. Token
c. Span
d. WordObj
Answer: b. Tokenspacy+1
What command lists all pipeline components of an nlp object?
a. nlp.list_components()
b. nlp.pipe_names
c. nlp.show()
d. nlp.describe()
Answer: b. nlp.pipe_namesdevopsschool+1
What will nlp(“Hello World!”) return?
a. Raw string
b. Doc object
c. Numeric vector
d. Boolean
Answer: b. Doc objectspacy+1
How do you add a custom component to the spaCy pipeline?
a. nlp.add_pipe(custom_component)
b. nlp.append(custom_component)
c. nlp.insert(custom_component)
d. nlp.pipe_add(custom_component)
Answer: a. nlp.add_pipe(custom_component)devopsschool+1
How to extract all named entities from a Doc?
a. doc.entities
b. doc.ents
c. doc.NE
d. doc.labels
Answer: b. doc.entsspacy+1
Which library is mainly used for converting spaCy objects to JSON?
a. json
b. pickle
c. pandas
d. xml
Answer: a. jsondevopsschool+1
What does spaCy’s “ner” component do?
a. Named Entity Recognition
b. Named Entity Removal
c. Part-of-speech tagging
d. Lemmatization
Answer: a. Named Entity Recognitionspacy+1
What does “vector” attribute in token provide in spaCy?
a. Sentence boundary
b. Dense word embedding
c. POS tag
d. Entity label
Answer: b. Dense word embeddingwayground+1
Which spaCy function saves a model to disk?
a. nlp.export()
b. nlp.write()
c. nlp.to_disk()
d. nlp.dump()
Answer: c. nlp.to_disk()devopsschool+1
How would you load a model from disk in spaCy?
a. nlp.load()
b. spacy.load()
c. model.open()
d. model.import()
Answer: b. spacy.load()spacy+1
Which of the following tasks is NOT covered by spaCy?
a. Tokenization
b. POS tagging
c. Named Entity Recognition
d. Optical Character Recognition
Answer: d. Optical Character Recognitiondevopsschool+1
Which class in spaCy can be used to create custom tokens?
a. Tokenizer
b. Documenter
c. Spaner
d. TokenizerFactory
Answer: a. Tokenizerspacy+1
What does spaCy’s Sentencizer component do?
a. Finds sentence boundaries
b. Finds tokens
c. Classifies entities
d. Predicts POS tags
Answer: a. Finds sentence boundariesdevopsschool+1
What is the function of the Matcher component?
a. Comparing word embeddings
b. Finding sequences of tokens matching rules
c. Classifying sentences
d. Translating documents
Answer: b. Finding sequences of tokens matching rulesspacy+1
How is the pipeline customized for domain-specific tasks?
a. Change computer hardware
b. Fine-tune or retrain pipeline components
c. Use more memory
d. Set POS only
Answer: b. Fine-tune or retrain pipeline componentsdevopsschool+1
Which spaCy command lists supported languages?
a. spacy.languages()
b. spacy.info()
c. spacy.explain()
d. spacy.lang
Answer: d. spacy.langspacy+1
What is the main advantage of spaCy’s design?
a. Focus on deep learning only
b. Focus on production-readiness and efficiency
c. Focus on visualization
d. Focus on speech recognition
Answer: b. Focus on production-readiness and efficiencygeeksforgeeks+1
What do you use doc.similarity(other_doc) for?
a. Comparing two numbers
b. Comparing similarity between documents
c. Printing output
d. Running system commands
Answer: b. Comparing similarity between documentsdevopsschool+1
What would “token.is_stop” attribute tell you?
a. Token is punctuation
b. Token is a named entity
c. Token is a stop word
d. Token is at document start
Answer: c. Token is a stop wordspacy+1
What does the spaCy “Lemmatizer” do?
a. Finds word endings
b. Assigns base forms to words
c. Computes embeddings
d. Assigns POS tags
Answer: b. Assigns base forms to wordsdevopsschool+1
spaCy provides pre-trained pipelines for which types of tasks?
a. Hardware control
b. NLP tasks (POS, NER, parsing)
c. Image processing
d. Game development
Answer: b. NLP tasks (POS, NER, parsing)spacy+1
Which of these libraries can spaCy integrate with for statistical models?
a. TensorFlow
b. Only with PyTorch
c. Only scikit-learn
d. None
Answer: a. TensorFlowprojectpro+1
How do you disable a particular component in spaCy pipeline?
a. nlp.stop()
b. nlp.disable_pipes()
c. nlp.skip()
d. nlp.remove_component()
Answer: b. nlp.disable_pipes()devopsschool+1
“Doc.cats” in spaCy represents:
a. Named entities
b. Sentence boundaries
c. Document-level categories
d. Tokens
Answer: c. Document-level categoriesspacy+1
How does spaCy handle out-of-vocabulary (OOV) words in embeddings?
a. Returns random vectors
b. Uses zero vectors
c. Raises errors
d. Returns None
Answer: b. Uses zero vectorsdevopsschool+1
When is a SpaCy pipeline component called “trainable”?
a. If its weights are fixed
b. If it can be updated with new data
c. If it’s rule-based only
d. Never
Answer: b. If it can be updated with new dataspacy+1
What is the latest recommended way to add an entity type in custom NER models in spaCy?
a. Manual text addition
b. Using EntityRuler
c. Using regular expressions only
d. It’s not allowed
Answer: b. Using EntityRulerdevopsschool+1

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