AI0-001 Implementing AI Solutions Practice Question
A company is building a RAG-based Q&A system for a large collection of technical manuals. They need to choose an embedding model and a similarity search method. Which TWO choices are most appropriate for this scenario? (Select TWO)
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
✓
Use a domain-specific embedding model fine-tuned on technical documentation
Cosine similarity is the standard metric for comparing embeddings (normalized vectors). Using a domain-specific embedding model (e.g., fine-tuned on technical text) yields better retrieval accuracy. Dot product can be used but is less common; Euclidean distance is not ideal for high-dimensional embeddings; a general-purpose model may perform poorly on domain-specific language.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a general-purpose embedding model like text-embedding-ada-002
Why it's wrong here
While it may work, a domain-specific embedding model fine-tuned on technical manuals would likely yield better retrieval accuracy.
- ✗
Use dot product as the similarity metric for non-normalized embeddings
Why it's wrong here
Dot product can be used but is less common; if embeddings are normalized, cosine similarity is equivalent but more interpretable.
- ✗
Use Euclidean distance as the similarity metric for vector search
Why it's wrong here
Euclidean distance is sensitive to vector magnitude; embeddings are often normalized, making cosine similarity more effective.
- ✓
Use a domain-specific embedding model fine-tuned on technical documentation
Why this is correct
A domain-specific model captures the nuances of technical language, improving retrieval precision for the Q&A system.
- ✓
Use cosine similarity as the similarity metric for vector search
Why this is correct
Cosine similarity measures the angle between vectors, which is standard for comparing embeddings, especially when using normalized vectors.
About these practice questions
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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.