AI0-001 Implementing AI Solutions Practice Question
A developer is implementing a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model produces normalized vectors. Which metric is computationally efficient and equivalent to cosine similarity for normalized vectors?
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, dot product is equivalent to cosine similarity and is faster to compute.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Euclidean distance
Why it's wrong here
Euclidean distance is not equivalent to cosine similarity even for normalized vectors; it measures magnitude differences.
- ✗
Hamming distance
Why it's wrong here
Hamming distance is used for categorical or binary data, not for real-valued embeddings.
- ✗
Manhattan distance
Why it's wrong here
Manhattan distance is a different metric; it does not correspond to cosine similarity.
- ✓
Dot product
Why this is correct
When vectors are unit normalized, dot product equals cosine similarity, and it is computationally efficient.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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