MCQs

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Overall MCQs for Reference

What is the primary objective of a Natural Language Model (NLM) in machine learning?
a. Image classification
b. Predicting word sequences
c. Market segmentation
d. IoT device management
Answer: b. Predicting word sequencesvertiv+1
Which task is commonly solved using language models in NLP?
a. Text generation
b. Image clustering
c. Structural engineering
d. Weather forecasting
Answer: a. Text generationgeeksforgeeks
N-gram models are a foundational method for predicting:
a. Video pixels
b. Next word in a sequence
c. Medical images
d. Stock prices
Answer: b. Next word in a sequencevertiv+1
In NLMs, word embeddings are used to:
a. Classify images
b. Represent words as vectors
c. Encode audio
d. Optimize hardware
Answer: b. Represent words as vectorsvertiv
Which is NOT an application of Natural Language Models?
a. Sentiment analysis
b. Machine translation
c. Traffic prediction
d. Speech recognition
Answer: c. Traffic predictiongeeksforgeeks+1
Which model architecture rose to prominence for contextual text processing?
a. Decision trees
b. Transformer
c. K-means
d. Naive Bayes
Answer: b. Transformeraimcqs+1
What does the “N” represent in “N-gram” models?
a. Number of characters
b. Number of words in context
c. Number of paragraphs
d. Number of sentences
Answer: b. Number of words in contextvertiv
Which technique improves machine translation models using monolingual data?
a. Transfer learning
b. Image augmentation
c. Reinforcement reward
d. Hash encoding
Answer: a. Transfer learningaimcqs
Language modeling can be statistically achieved by:
a. Random search
b. Probability distribution over words
c. Binary encoding
d. Pixel mapping
Answer: b. Probability distribution over wordsgeeksforgeeks+1
Which neural model architecture can handle long-range dependencies in text?
a. Linear Regression
b. LSTM
c. Decision Tree
d. K-means
Answer: b. LSTMaimcqs+1
How are modern language models different from traditional statistical models?
a. Use image data
b. Leverage large neural networks
c. Rely only on bigrams
d. Only process numerical signals
Answer: b. Leverage large neural networksgeeksforgeeks+1
Byte Pair Encoding (BPE) is mainly used for:
a. Dimensionality reduction
b. Subword tokenization
c. Feature mapping
d. Clustering
Answer: b. Subword tokenizationaimcqs
Which of the following is a pre-trained language model?
a. SVM
b. GPT-3
c. DBSCAN
d. Random Forest
Answer: b. GPT-3geeksforgeeks
What role do pre-trained NLMs play in NLP tasks?
a. Eliminate need for labeled data
b. Leverage learned knowledge
c. Optimize hardware
d. Improve routing protocols
Answer: b. Leverage learned knowledgegeeksforgeeks
Which of these is a common unsupervised algorithm in natural language modeling?
a. K-means clustering
b. SVM
c. XGBoost
d. Linear Regression
Answer: a. K-means clusteringanalytixlabs+1
In text classification, which algorithm is commonly used?
a. CNN
b. LSA
c. BERT
d. LDA
Answer: b. LSAaimcqs
Sentiment analysis is best accomplished with:
a. NLMs trained on labeled sentiment data
b. Time-series models
c. Auto encoders
d. Ensemble trees
Answer: a. NLMs trained on labeled sentiment datageeksforgeeks+1
Which attention mechanism benefits neural language models?
a. Reduces overfitting
b. Prioritizes relevant input parts
c. Lowers hardware cost
d. Increases input length
Answer: b. Prioritizes relevant input partsaimcqs
Speech recognition systems commonly use:
a. NLMs for sequence prediction
b. Image classifiers
c. WordNet only
d. Decision trees
Answer: a. NLMs for sequence predictiongeeksforgeeks+1
Which is a challenge in NLP tackled by NLMs?
a. Handling ambiguity
b. Hardware scaling
c. CPU scheduling
d. Schema normalization
Answer: a. Handling ambiguitygtbit+1
Named Entity Recognition (NER) is supported by:
a. Language models for context
b. Histogram equalization
c. Regression models
d. Only statistical clustering
Answer: a. Language models for contextgeeksforgeeks+1
Which model is best for hierarchical text categorization?
a. Decision Tree
b. Hierarchical NLM
c. CNN
d. PCA
Answer: b. Hierarchical NLMpmc.ncbi.nlm.nih+1
Which process converts language into numerical format for machine understanding?
a. Word embedding
b. Hashing
c. Image encoding
d. Data normalization
Answer: a. Word embeddingvertiv
What is a key advantage of neural language models?
a. Scalability and contextual accuracy
b. Fast sorting algorithms
c. Improved RGB color mapping
d. Linear transformations
Answer: a. Scalability and contextual accuracygeeksforgeeks+1
Which model is foundational for auto-complete functions?
a. N-gram language model
b. SVM
c. Principal Component Analysis
d. CNN
Answer: a. N-gram language modelvertiv+1
Machine translation is possible due to:
a. Sequence-to-sequence NLMs
b. Random forests
c. Image clustering
d. Regression models
Answer: a. Sequence-to-sequence NLMsgeeksforgeeks+1
Transformers outperform LSTM due to their:
a. Better parallelization and context handling
b. Color depth encoding
c. Fewer parameters
d. Increased randomness
Answer: a. Better parallelization and context handlingaimcqs+1
Which unsupervised technique is used for topic modeling?
a. Latent Dirichlet Allocation (LDA)
b. SVM
c. NLMs clustering
d. Gradient boosting
Answer: a. Latent Dirichlet Allocation (LDA)analytixlabs+1
Semantic similarity between texts is measured using:
a. Vector space models
b. RGB differentials
c. Image histograms
d. Token counting
Answer: a. Vector space modelsvertiv
Which rule-based NLP technique is commonly complemented by NLMs?
a. Parsing
b. Color balancing
c. Feature engineering
d. Pixel binning
Answer: a. Parsingibm+1
For document classification, NLMs use:
a. Bag-of-Words or embeddings
b. Time-series data
c. Graph networks
d. Random search
Answer: a. Bag-of-Words or embeddingsgeeksforgeeks+1
What is a key step during NLM training?
a. Computing word probabilities
b. Creating image pixels
c. Optimizing cache
d. Reducing sentence lengths
Answer: a. Computing word probabilitiesvertiv+1
Which model best predicts the next word in a sentence with context?
a. Transformer
b. Linear Regression
c. CNN
d. PCA
Answer: a. Transformeraimcqs+1
Human-like comprehension in AI is enabled by:
a. Advanced neural language models
b. CPU upgrades
c. Fully supervised algorithms
d. Pixel optimization
Answer: a. Advanced neural language modelsvertiv
What is “tokenization” in NLP?
a. Dividing text into smaller units
b. Image resizing
c. Network routing
d. Hashing text
Answer: a. Dividing text into smaller unitsaimcqs+1
Which is a fundamental evaluation metric for NLMs?
a. Perplexity
b. RMS error
c. Color histogram
d. DBSCAN distance
Answer: a. Perplexitygeeksforgeeks+1
BERT is an example of:
a. Transformer-based neural language model
b. SVM
c. Pure statistical model
d. Ensemble tree
Answer: a. Transformer-based neural language modelaimcqs+1
Contextualization in NLMs refers to:
a. Considering surrounding words for predictions
b. CPU scheduling
c. Database indexing
d. Reducing noise
Answer: a. Considering surrounding words for predictionsgeeksforgeeks+1
Language model “fine-tuning” involves:
a. Adapting pre-trained model to a specific task
b. Hardware tuning
c. Feature selection
d. Network configuration
Answer: a. Adapting pre-trained model to a specific taskibm+1
Which of the following is NOT a typical use of NLMs?
a. Text summarization
b. Speech generation
c. Crop rotation planning
d. Information retrieval
Answer: c. Crop rotation planningvertiv+1
Sequence-to-sequence models help with:
a. Translation and dialogue systems
b. Static image classification
c. Hardware routing
d. Peer-to-peer networking
Answer: a. Translation and dialogue systemsgeeksforgeeks+1
Part-of-speech tagging addresses:
a. Labeling grammatical categories
b. Image segmentation
c. Color encoding
d. Cache optimization
Answer: a. Labeling grammatical categoriesgeeksforgeeks+1
What mechanism in transformers allows selectivity?
a. Attention
b. Regularization
c. Feature mapping
d. Pooling
Answer: a. Attentionaimcqs+1
Which language model approach uses both past and future context?
a. Bidirectional Transformer
b. Linear Regression
c. Unigram model
d. RGB space model
Answer: a. Bidirectional Transformervertiv+1
Which is an advantage of transfer learning with NLMs?
a. Reduced data requirements
b. Faster pixel computation
c. Singular vector encoding
d. Hardware parallelism
Answer: a. Reduced data requirementsgeeksforgeeks+1
Which problem can NLMs address in customer support?
a. Automated chat responses
b. Hardware upgrades
c. Network routing
d. Color depth encoding
Answer: a. Automated chat responsesibm+1
Language models enable which feature in smart devices?
a. Voice assistants
b. Battery optimization
c. Touch response
d. Screen calibration
Answer: a. Voice assistantsibm+1
One important challenge for NLMs is:
a. Handling polysemy
b. Pixel compression
c. Voltage control
d. XGBoost tuning
Answer: a. Handling polysemygtbit+1
Named entity recognition models find:
a. Specific names, organizations, places in text
b. Image boundaries
c. Network protocols
d. Pixel clusters
Answer: a. Specific names, organizations, places in textgeeksforgeeks+1
What is the output format of most NLMs in NLP tasks?
a. Probability distribution over possible next tokens
b. Image arrays
c. Network graphs
d. Audio waveforms
Answer: a. Probability distribution over possible next tokensgeeksforgeeks+1

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