AI0-001 Implementing AI Solutions Practice Question
Which component in a RAG system is responsible for converting document chunks into numerical representations that enable similarity search?
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
✓
Embedding model
An embedding model (or encoder) transforms text into dense vectors. The vector store indexes these vectors, and the LLM generates answers. Chunking is the splitting step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Vector store index
Why it's wrong here
The index organizes vectors for efficient search but does not create them.
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Document chunker
Why it's wrong here
The chunker splits documents into pieces but does not create numerical representations.
- ✗
Large language model (LLM)
Why it's wrong here
The LLM generates final answers, not embeddings for retrieval.
- ✓
Embedding model
Why this is correct
The embedding model converts text chunks into vector embeddings.
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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.