AI0-001 Implementing AI Solutions Practice Question
Which embedding type is MOST suitable for capturing semantic meaning of text in a RAG pipeline?
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
✓
Dense embeddings from a pre-trained transformer model
Dense embeddings represent semantic meaning in a continuous vector space, ideal for similarity search in RAG.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bag-of-words vectors
Why it's wrong here
Bag-of-words is sparse and ignores word order and semantics, leading to poor retrieval relevance.
- ✓
Dense embeddings from a pre-trained transformer model
Why this is correct
Dense embeddings capture contextualized semantic meaning, enabling effective similarity search.
- ✗
TF-IDF vectors
Why it's wrong here
TF-IDF is sparse and based on term frequency; it does not capture deep semantic relationships.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is sparse and does not capture semantic similarity; it treats each word as independent.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.