AI0-001 AI Concepts and Foundations Practice Question
A team is building a natural language processing (NLP) model to analyze customer feedback. They have a large corpus of unlabeled text data and want to generate word embeddings that capture semantic meaning. Which approach should they use?
⚠ Common exam trap
CompTIA often tests the distinction between frequency-based vectorization (TF-IDF, bag-of-words) and prediction-based embedding methods (Word2Vec, GloVe), trapping candidates who think TF-IDF captures semantic meaning when it only captures term importance in a document.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Word2Vec
Word2Vec is the correct approach because it learns dense, distributed word embeddings from large unlabeled corpora by training a shallow neural network to predict words in context (CBOW) or context from words (Skip-gram). This captures semantic relationships such as analogy and similarity, which is essential for analyzing customer feedback without labeled data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates high-dimensional sparse vectors with no semantic meaning.
- ✗
TF-IDF vectorization
Why it's wrong here
TF-IDF produces sparse vectors and does not capture semantic similarity between words.
- ✓
Word2Vec
Why this is correct
Word2Vec learns dense embeddings from unlabeled text, capturing semantic relationships.
- ✗
Bag-of-words model
Why it's wrong here
Bag-of-words ignores word order and semantics, resulting in sparse representations.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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