AI0-001 Implementing AI Solutions Practice Question
A developer is building a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model they use produces normalized vectors (unit vectors). Which similarity metric is equivalent to cosine similarity in this case?
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
✓
Dot product
For normalized vectors, cosine similarity and dot product are equivalent because the dot product equals the cosine of the angle times the product of magnitudes (which are 1).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Jaccard similarity
Why it's wrong here
Jaccard similarity is for sets, not vectors.
- ✗
Euclidean distance
Why it's wrong here
Euclidean distance is a distance metric, not directly equivalent to cosine similarity even for unit vectors.
- ✗
Manhattan distance
Why it's wrong here
Manhattan distance is not equivalent to cosine similarity.
- ✓
Dot product
Why this is correct
For unit vectors, dot product equals cosine similarity because ||a|| ||b|| = 1.
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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.