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 produces sparse orthogonal vectors with no shared structure, so it cannot capture semantic similarity between words. It is tempting as a basic text-encoding step, and it would be correct for representing categorical variables or small vocabularies in simple models.
- ✗
TF-IDF vectorization
Why it's wrong here
TF-IDF weights terms by frequency and rarity, yielding sparse vectors whose dimensions remain independent, so semantic relationships are not encoded. It is tempting because it is a standard text-vectorisation technique, and it would be correct for document classification or keyword retrieval tasks.
- ✓
Word2Vec
Why this is correct
Word2Vec learns dense vector representations from unlabelled text by predicting a word from its neighbours (skip-gram) or vice versa (CBOW), so semantic relationships emerge from co-occurrence statistics. This directly satisfies the stem's requirement for embeddings from a large unlabelled corpus, unlike supervised approaches needing labelled data.
- ✗
Bag-of-words model
Why it's wrong here
Bag-of-words counts token occurrences and discards order and context, producing sparse vectors with no semantic relationships between terms. It is tempting as a simple text representation, and it would be correct for basic sentiment classification or topic detection with small datasets.
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