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NLP

Topic in AI / Machine Learning & Data Analytics

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About This Topic

Natural language processing (NLP) is the area of AI that enables computers to read, interpret and generate human language in text or speech. Preprocessing questions cover tokenization, stop-word removal, stemming versus lemmatization, and part-of-speech tagging. Representation items compare bag-of-words, TF-IDF and n-grams with dense word embeddings like Word2Vec, GloVe and fastText. Expect tasks such as named entity recognition, sentiment analysis and machine translation, and model families from RNN sequence-to-sequence to transformers. BERT's masked language modelling objective and GPT's next-token prediction appear often, along with evaluation metrics such as BLEU and ROUGE and their limits for open-ended text generation.

Below are 30 practice questions from a pool of 210 NLP MCQs, one of 17 topics in AI / Machine Learning & Data Analytics. Each shows the correct answer with an explanation; when you are ready, take a timed quiz to test recall under exam conditions.

Practice Questions

Each question below shows the correct answer with a full explanation. Use these to build conceptual understanding before attempting a timed quiz.

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Q1. What does NLP stand for?

  1. A.Numerical Linear Processing
  2. B.Network Logic Protocol
  3. C.Natural Language Processing✓ Correct
  4. D.Neural Language Programming

Explanation

NLP stands for Natural Language Processing, the field of AI dealing with interaction between computers and human language.

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Q2. Named Entity Recognition (NER) identifies:

  1. A.Grammar errors found throughout the given text✓ Correct
  2. B.Spelling mistakes identified within the text body
  3. C.The overall length of each sentence in the document
  4. D.Named entities like people, organizations, and locations in text

Explanation

NER identifies and classifies named entities in text into predefined categories such as person, organization, location, and date.

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Q3. What is stemming?

  1. A.Reducing words to their root form by removing suffixes✓ Correct
  2. B.Adding prefixes to words for morphological expansion
  3. C.Counting the total frequency of words in text
  4. D.Translating words between two different languages

Explanation

Stemming removes suffixes to reduce words to their root form (e.g., 'running' -> 'run'), simplifying text for processing.

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Q4. What is a corpus in NLP?

  1. A.A large collection of text documents✓ Correct
  2. B.A specific grammar rule definition
  3. C.A single word in a sentence
  4. D.A type of neural network model

Explanation

A corpus is a large, structured collection of text documents used for training and evaluating NLP models.

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Q5. What is lemmatization?

  1. A.Counting the total characters in a word
  2. B.Removing all vowels from the input text
  3. C.Adding suffixes for morphological inflection
  4. D.Reducing words to their base dictionary form✓ Correct

Explanation

Lemmatization reduces words to their base dictionary form (lemma) using vocabulary and morphological analysis (e.g., 'better' -> 'good').

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Q6. Tokenization in NLP means:

  1. A.Encrypting text for secure data transmission
  2. B.Splitting text into individual words or subwords✓ Correct
  3. C.Deleting text from the data pipeline
  4. D.Compressing text to reduce storage size

Explanation

Tokenization breaks text into smaller units (tokens) such as words, subwords, or characters for processing by NLP models.

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Q7. Stop words are:

  1. A.Important domain-specific keywords carrying high information
  2. B.Common words like 'the' and 'is' often removed in processing✓ Correct
  3. C.Rare words that appear only once in the entire corpus
  4. D.Technical terms from a specialized subject vocabulary

Explanation

Stop words are frequently occurring words that carry little meaning and are often removed during text preprocessing to focus on content words.

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Q8. What is sentiment analysis?

  1. A.Summarizing long documents into shorter text passages
  2. B.Translating text from one natural language to another language
  3. C.Generating entirely new text from a given prompt input
  4. D.Determining the emotional tone of text (positive, negative, neutral)✓ Correct

Explanation

Sentiment analysis identifies the emotional tone or opinion expressed in text, classifying it as positive, negative, or neutral.

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Q9. What is text classification?

  1. A.Generating entirely new text from a prompt
  2. B.Translating text between different languages
  3. C.Editing and correcting text for grammar
  4. D.Assigning predefined categories to text documents✓ Correct

Explanation

Text classification assigns predefined labels or categories to text documents, such as spam detection or topic classification.

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Q10. What is machine translation?

  1. A.Performing manual text translation
  2. B.Generating new text from a prompt
  3. C.Summarizing text into shorter form✓ Correct
  4. D.Automatically translating text from one language to another

Explanation

Machine translation uses AI to automatically translate text between languages, as in Google Translate.

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Q11. What is the bag-of-words model?

  1. A.Representing text with full word order preserved
  2. B.A syntactic parsing technique for sentences
  3. C.Representing text as the frequency of words, ignoring order✓ Correct
  4. D.A neural language model using word embeddings

Explanation

Bag-of-words represents text as a vector of word frequencies, disregarding grammar and word order while capturing word presence.

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Q12. What is Word2Vec?

  1. A.A simple word frequency counter for bag-of-words features
  2. B.A technique learning dense vector representations of words from context✓ Correct
  3. C.A language dictionary for looking up word definitions
  4. D.A rule-based grammar checker for text correction

Explanation

Word2Vec learns dense vector representations (embeddings) of words where semantically similar words are mapped to nearby points in vector space.

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Q13. What is attention in sequence-to-sequence models?

  1. A.A data augmentation method for creating synthetic text training data
  2. B.A mechanism allowing the decoder to focus on relevant parts of the input sequence✓ Correct
  3. C.A cross-entropy loss function for measuring sequence prediction accuracy
  4. D.A regularization technique for preventing overfitting in sequence models

Explanation

Attention allows the decoder to attend to different parts of the input sequence when generating each output token, improving translation and generation quality.

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Q14. What is TF-IDF?

  1. A.A recurrent neural network variant designed for sequential text modeling
  2. B.A structured query language for relational database data retrieval
  3. C.A multi-layer deep feedforward neural network architecture for classification
  4. D.A weighting scheme reflecting word importance in a document relative to a corpus✓ Correct

Explanation

TF-IDF (Term Frequency-Inverse Document Frequency) weighs words by how often they appear in a document relative to how common they are across all documents.

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Q15. What is text embedding?

  1. A.Converting text into dense numerical vectors✓ Correct
  2. B.Removing text from the raw dataset
  3. C.Encrypting text for secure storage
  4. D.Converting numbers into readable text

Explanation

Text embedding converts text into dense numerical vector representations that capture semantic meaning, enabling mathematical operations on text.

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Q16. What is sequence-to-sequence (Seq2Seq) modeling?

  1. A.Mapping a single input word to a single corresponding output word
  2. B.Sorting input sequences into a predefined correct order
  3. C.Clustering multiple sequences into meaningful related groups✓ Correct
  4. D.Mapping an input sequence to an output sequence of potentially different length

Explanation

Seq2Seq models transform an input sequence into an output sequence, used in translation, summarization, and dialogue generation.

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Q17. What is BERT?

  1. A.A rule-based expert system for language processing
  2. B.A generative adversarial network designed for text data
  3. C.A pre-trained transformer for bidirectional language understanding✓ Correct
  4. D.A type of recurrent neural network for sequential processing

Explanation

BERT (Bidirectional Encoder Representations from Transformers) is pre-trained on masked language modeling and next sentence prediction for bidirectional context understanding.

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Q18. What is part-of-speech (POS) tagging?

  1. A.Removing individual words from the input text sequence
  2. B.Translating text between two different natural languages
  3. C.Assigning grammatical categories (noun, verb, etc.) to each word✓ Correct
  4. D.Counting the number of syllables in each word

Explanation

POS tagging assigns grammatical labels like noun, verb, adjective to each word in a sentence, aiding in syntactic analysis.

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Q19. What is the difference between GPT and BERT architectures?

  1. A.Both use exactly the same attention mechanism and training objective
  2. B.GPT uses bidirectional attention; BERT uses causal left-to-right attention
  3. C.They are completely identical transformer architectures with no differences
  4. D.GPT uses causal attention for generation; BERT uses bidirectional attention for understanding✓ Correct

Explanation

GPT uses masked (causal) self-attention for autoregressive text generation. BERT uses bidirectional self-attention for understanding tasks, seeing context from both directions.

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Q20. What is the perplexity metric in language models?

  1. A.The total elapsed time required for training the model
  2. B.The number of parameters or total size of the model
  3. C.A measure of how well a model predicts a sample; lower is better✓ Correct
  4. D.The total number of words in the vocabulary of the model

Explanation

Perplexity measures how well a probability distribution predicts a sample. Lower perplexity indicates the model is better at predicting the text.

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Q21. What is cosine similarity used for in NLP?

  1. A.Counting the total frequency of words in text
  2. B.Parsing sentences into syntax tree structures
  3. C.Generating new text from a trained model
  4. D.Measuring the similarity between two text vectors✓ Correct

Explanation

Cosine similarity measures the cosine of the angle between two vectors, indicating how similar two texts are regardless of their magnitude.

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Q22. What is byte-pair encoding (BPE) in tokenization?

  1. A.A tokenization approach that only works at the word level
  2. B.A tokenization strategy that works at sentence level✓ Correct
  3. C.A tokenization method operating at character level only
  4. D.A subword tokenization algorithm that iteratively merges the most frequent character pairs

Explanation

BPE starts with characters and iteratively merges the most frequent adjacent pairs to create subword units, balancing vocabulary size and out-of-vocabulary handling.

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Q23. What is cross-lingual transfer learning?

  1. A.Translating all text first then training on the translated version
  2. B.Ignoring language differences and treating all text the same
  3. C.Training on one language and applying knowledge to another language✓ Correct
  4. D.Training separate independent models for each individual language

Explanation

Cross-lingual transfer leverages knowledge learned from one language to improve performance on another, often using multilingual pre-trained models like mBERT or XLM-R.

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Q24. What is the problem of hallucination in large language models?

  1. A.Generating plausible-sounding but factually incorrect or fabricated information✓ Correct
  2. B.Using too much GPU memory during the inference computation
  3. C.Producing completely empty outputs with no generated tokens
  4. D.Generating text output at an excessively slow processing speed

Explanation

Hallucination refers to LLMs generating confident, fluent text that contains fabricated facts, incorrect details, or unsupported claims.

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Q25. What is a language model?

  1. A.A static vocabulary dictionary for word lookup
  2. B.A rule-based grammar checker for syntax only
  3. C.A database of aligned translation memory pairs
  4. D.A model that predicts the probability of a sequence of words✓ Correct

Explanation

A language model estimates the probability distribution over sequences of words, used for text generation, completion, and evaluation of sentence fluency.

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Q26. What is the concept of positional encoding in Transformers?

  1. A.Normalizing the positional coordinates of data in feature space
  2. B.Encoding the physical position of GPUs in a server rack cluster
  3. C.Adding position info to embeddings since Transformers lack inherent sequence order✓ Correct
  4. D.Sorting the input data samples by their index in the dataset

Explanation

Since Transformers process all positions in parallel (no recurrence), positional encodings add sequence order information to input embeddings using sinusoidal functions or learned embeddings.

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Q27. What is retrieval-augmented generation (RAG)?

  1. A.Combining retrieval with generation to ground outputs in external knowledge✓ Correct
  2. B.An unsupervised clustering method for grouping similar documents
  3. C.A purely generative approach relying entirely on internal knowledge
  4. D.A purely retrieval approach returning documents without generation

Explanation

RAG retrieves relevant documents from a knowledge base and uses them as context for the language model's generation, reducing hallucination and enabling knowledge updates.

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Q28. What is the difference between extractive and abstractive summarization?

  1. A.Extractive generates completely new text from the source material
  2. B.Abstractive only selects existing sentences from the original text
  3. C.Extractive and abstractive summarization are identical methods✓ Correct
  4. D.Extractive selects existing sentences; abstractive generates new sentences

Explanation

Extractive summarization selects key sentences from the source text. Abstractive summarization generates new sentences that capture the main ideas, potentially using words not in the original.

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Q29. What is prompt engineering?

  1. A.Cleaning and preprocessing the raw input training data
  2. B.Modifying the internal neural network model architecture
  3. C.Training the language model from scratch on new domain data
  4. D.Designing effective input prompts to guide LLM behavior without fine-tuning✓ Correct

Explanation

Prompt engineering crafts input prompts that effectively guide pre-trained language models to produce desired outputs without modifying model weights.

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Q30. What is the RLHF (Reinforcement Learning from Human Feedback) technique?

  1. A.Unsupervised pre-training using masked language modeling objectives
  2. B.Standard supervised fine-tuning using labeled input-output training pairs
  3. C.Data augmentation using paraphrasing and back-translation methods
  4. D.Fine-tuning language models using human preference judgments as reward signals✓ Correct

Explanation

RLHF trains a reward model from human preference comparisons and uses reinforcement learning to fine-tune the language model to generate outputs aligned with human preferences.

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