CAIC CAIC Natural Language Processing & AI Applications 1 — Questions and Answers
Question 1: Which NLP technique splits text into individual words or tokens for further processing?
- Lemmatization
- Tokenization (Correct answer)
- Stemming
- Parsing
Correct answer: Tokenization
Tokenization is the process of breaking text into individual words, phrases, or symbols called tokens.
Question 2: What does TF-IDF measure in text analysis?
- Sentence sentiment polarity
- Word importance relative to a document corpus (Correct answer)
- Topic cluster similarity
- Named entity frequency
Correct answer: Word importance relative to a document corpus
TF-IDF (Term Frequency–Inverse Document Frequency) quantifies how important a word is to a document relative to a collection of documents.
Question 3: Which model architecture introduced the concept of self-attention and became the foundation for large language models?
- LSTM
- CNN
- Transformer (Correct answer)
- RNN
Correct answer: Transformer
The Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need,' uses self-attention mechanisms and underpins modern LLMs.
Question 4: What is 'prompt engineering' in the context of AI consulting?
- Writing software prompts for user interfaces
- Designing input instructions to guide LLM outputs effectively (Correct answer)
- Configuring database query templates
- Setting up CI/CD pipeline triggers
Correct answer: Designing input instructions to guide LLM outputs effectively
Prompt engineering is the practice of crafting and optimizing text inputs to elicit accurate, relevant, and controlled responses from large language models.
Question 5: Which evaluation metric is commonly used to assess the quality of machine-generated text against reference text?
- RMSE
- BLEU score (Correct answer)
- F1 score
- AUC-ROC
Correct answer: BLEU score
BLEU (Bilingual Evaluation Understudy) score compares n-gram overlap between generated and reference text and is widely used for NLP tasks like translation.
Question 6: What is the primary business use case for sentiment analysis in an AI consulting engagement?
- Detecting network intrusions
- Extracting customer opinions from reviews or social media (Correct answer)
- Optimizing database indexing
- Predicting hardware failures
Correct answer: Extracting customer opinions from reviews or social media
Sentiment analysis classifies text as positive, negative, or neutral, enabling businesses to gauge customer satisfaction and brand perception at scale.
CAIC CAIC Natural Language Processing & AI Applications 1
Which NLP technique splits text into individual words or tokens for further processing?