MCQs

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Overall MCQs for Reference

What is the primary goal of Natural Language Processing (NLP)?
a. Building hardware systems
b. Understanding and processing human language
c. Designing web pages
d. Creating databases
Answer: b. Understanding and processing human languageplacementpreparation+1
Which NLP task focuses on extracting subject, object, and predicate from a sentence?
a. Sentiment Analysis
b. Dependency Parsing
c. Text Summarization
d. Language Modeling
Answer: b. Dependency Parsinggeeksforgeeks
What technique in NLP breaks text into individual tokens?
a. Chunking
b. Tokenization
c. Lemmatization
d. POS Tagging
Answer: b. Tokenizationlexalytics+1
Which of the following is a pre-trained language model commonly used in NLP?
a. BERT
b. KNN
c. Apriori
d. SVM
Answer: a. BERTgeeksforgeeks+1
Which algorithm is commonly used for Named Entity Recognition in NLP?
a. K-means clustering
b. Hidden Markov Models (HMM)
c. Apriori Algorithm
d. Decision Trees
Answer: b. Hidden Markov Models (HMM)aimcqs+1
What is the purpose of the Bag-of-Words (BoW) model in NLP?
a. Syntactic Parsing
b. Vectorizing text for ML tasks
c. Language Translation
d. Speech Recognition
Answer: b. Vectorizing text for ML tasksshelf+1
Which NLP technique converts words to their base or dictionary form?
a. Lemmatization
b. Stemming
c. Chunking
d. Parsing
Answer: a. Lemmatizationgeeksforgeeks+1
What is Part-of-Speech tagging in NLP?
a. Assigning polarity to text
b. Labeling grammatical categories to words
c. Extracting key phrases
d. Detecting entities
Answer: b. Labeling grammatical categories to wordsaimcqs+1
Which model is designed for sequence-to-sequence machine translation?
a. CNN
b. Naive Bayes
c. Transformer
d. SVM
Answer: c. Transformeroracle+1
Which of the following is NOT a core NLP task?
a. Speech Recognition
b. Machine Translation
c. Image Classification
d. Text Summarization
Answer: c. Image Classificationaimcqs+1
Which statistical model is popular for language modeling?
a. Hidden Markov Model
b. Decision Tree
c. Random Forest
d. KNN
Answer: a. Hidden Markov Modelshelf+1
Which technique improves translation systems by using monolingual data?
a. Transfer Learning
b. Stemming
c. Text Summarization
d. Chunking
Answer: a. Transfer Learningdatacamp+1
Word Sense Disambiguation in NLP deals with:
a. Extracting words from sentences
b. Assigning meaning to words based on context
c. Creating embeddings
d. Detecting sentences
Answer: b. Assigning meaning to words based on contextgeeksforgeeks+1
What are word embeddings used for in NLP?
a. Visualizing data
b. Representing words numerically
c. Generating speech
d. Building decision trees
Answer: b. Representing words numericallyoracle+1
Which method is used to classify text as spam or non-spam?
a. Topic Modeling
b. Sentiment Analysis
c. Spam Detection
d. Entity Linking
Answer: c. Spam Detectiondeeplearning+1
Which NLP method merges similar words to a single representation?
a. Stemming
b. Lemmatization
c. Tokenization
d. Chunking
Answer: a. Stemminggeeksforgeeks+1
The Latent Dirichlet Allocation (LDA) algorithm is best known for:
a. Part-of-Speech Tagging
b. Topic Modeling
c. Named Entity Recognition
d. Speech-to-Text
Answer: b. Topic Modelingprojectpro+1
TF-IDF in NLP stands for:
a. Text Frequency-Inverse Data Function
b. Term Frequency-Inverse Document Frequency
c. Tokenized Frequent Data Index
d. Text Formatted Identifier
Answer: b. Term Frequency-Inverse Document Frequencyshelf+1
Which of the following is considered a generative model in NLP?
a. Decision Tree
b. Naive Bayes
c. SVM
d. Random Forest
Answer: b. Naive Bayespangeanic+1
In sequence labeling tasks, what is the common evaluation metric?
a. Precision-Recall
b. BLEU Score
c. F1 Score
d. Accuracy
Answer: c. F1 Scoreprojectpro+1
Which neural network is widely used for sentiment analysis?
a. CNN
b. RNN
c. KNN
d. DBSCAN
Answer: b. RNNpangeanic+1
What is Byte Pair Encoding (BPE) used for in NLP?
a. Tokenization
b. Word Sense Disambiguation
c. Text Summarization
d. Chunking
Answer: a. Tokenizationaimcqs
Which statistical technique is commonly used for word clustering?
a. Apriori Algorithm
b. K-means
c. SVM
d. Decision Tree
Answer: b. K-meanspangeanic+1
Which NLP task focuses on extracting names, locations, and organizations?
a. Text Classification
b. Named Entity Recognition (NER)
c. Sentiment Analysis
d. Coreference Resolution
Answer: b. Named Entity Recognition (NER)geeksforgeeks+1
Which process converts written text to spoken language?
a. Text Summarization
b. Text Classification
c. Text-to-Speech (TTS)
d. Chunking
Answer: c. Text-to-Speech (TTS)oracle+1
Which NLP technique detects the overall mood or emotion of a text?
a. Spam Detection
b. Sentiment Analysis
c. Entity Linking
d. Word Embedding
Answer: b. Sentiment Analysisgeeksforgeeks+1
What is relationship extraction in NLP?
a. Discovering connections between entities
b. Topic Modeling
c. Stemming
d. Named Entity Recognition
Answer: a. Discovering connections between entitiesgeeksforgeeks+1
Which model handles long-term dependencies better in late 2010s NLP?
a. CNN
b. RNN
c. LSTM
d. Decision Tree
Answer: c. LSTMpangeanic+1
What does the BLEU score measure?
a. Speech accuracy
b. Translation quality
c. Sentiment polarity
d. Entity recognition accuracy
Answer: b. Translation qualitygeeksforgeeks
Which technique is used to break a text into grammatical phrases?
a. Chunking
b. Lemmatization
c. Stemming
d. Summarization
Answer: a. Chunkingaimcqs+1
Which of the following models is not a deep learning model for NLP?
a. GPT
b. BERT
c. Random Forest
d. LSTM
Answer: c. Random Forestoracle+1
What is one challenge unique to NLP compared to other ML tasks?
a. Numerical data input
b. Text ambiguity
c. Consistent token size
d. Easy feature extraction
Answer: b. Text ambiguitygtbit+1
Which algorithm uses transition probabilities for sequence prediction?
a. KNN
b. Hidden Markov Models
c. Decision Trees
d. SVM
Answer: b. Hidden Markov Modelsshelf+1
What is co-reference resolution?
a. Mapping words to clusters
b. Detecting repeated words
c. Identifying when two terms refer to the same entity
d. Sorting sentences
Answer: c. Identifying when two terms refer to the same entitygeeksforgeeks+1
Which is a primary feature of convolutional neural networks in NLP?
a. Sequential data handling
b. Spatial data extraction from text
c. Fast numeric computation
d. Large input dimension
Answer: b. Spatial data extraction from textpangeanic
What does multimodal NLP refer to?
a. NLP models using only text
b. Combining text with visual and audio inputs
c. Only speech-based NLP
d. Numeric NLP
Answer: b. Combining text with visual and audio inputsdatacamp
In NLP, what is a corpus?
a. A single document
b. A collection of texts
c. A model parameter
d. A type of algorithm
Answer: b. A collection of textsdeeplearning+1
Which NLP method is used for automatic summarization?
a. Entity detection
b. Text Summarization
c. Classification
d. Clustering
Answer: b. Text Summarizationgeeksforgeeks+1
What is ethical AI in the context of NLP?
a. Ensuring model randomness
b. Maintaining fairness, transparency, and accountability in NLP systems
c. Data encryption standards
d. Dataset augmentation
Answer: b. Maintaining fairness, transparency, and accountability in NLP systemsdatacamp
Which metric might be used to evaluate the output of summarization models?
a. ROUGE
b. BLEU
c. F1 Score
d. Accuracy
Answer: a. ROUGEgeeksforgeeks
What is one use of Support Vector Machines in NLP?
a. Clustering tokens
b. Classifying texts and emails
c. Reducing dimensions
d. Sequence generation
Answer: b. Classifying texts and emailsdeeplearning+1
Which is NOT a step in typical text preprocessing for NLP?
a. Removing stopwords
b. Tokenization
c. Data normalization
d. Image resizing
Answer: d. Image resizingdatacamp+1
Which method learns representations by word co-occurrence?
a. Latent Semantic Analysis (LSA)
b. SVM
c. KNN
d. Decision Trees
Answer: a. Latent Semantic Analysis (LSA)aimcqs+1
Which item is central to unsupervised NLP tasks?
a. Labeled training data
b. Model deployment
c. Unlabeled corpora
d. Speech synthesis
Answer: c. Unlabeled corporashelf+1
What type of learning is most common for entity extraction tasks?
a. Reinforcement Learning
b. Supervised Learning
c. Semi-supervised Learning
d. Unsupervised Learning
Answer: b. Supervised Learninggeeksforgeeks+1
Which technique improves data efficiency across NLP tasks?
a. Transfer Learning
b. Chunking
c. Bag-of-Words
d. Random Forest
Answer: a. Transfer Learningoracle+1
What does “Tokenization” directly produce?
a. Lemmatized forms
b. Language models
c. Word or phrase segments
d. Entity pairs
Answer: c. Word or phrase segmentslexalytics+1
Which is a challenge when deploying NLP models?
a. Variability in text input
b. Fast computation
c. Lack of training data
d. Consistent labeling
Answer: a. Variability in text inputgtbit+1
Which deep learning architecture is designed for parallel sequence processing?
a. Recurrent Neural Network
b. SVM
c. Transformer
d. Random Forest
Answer: c. Transformeroracle+1
Which of these is central to context-aware NLP applications?
a. Static word vectors
b. Contextual embeddings
c. Feature hashing
d. Image recognition
Answer: b. Contextual embeddingsgeeksforgeeks+1

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